Claves en la aplicación del algoritmo Chaid : un estudio del ocio físico deportivo universitario

The purpose of this article is to explain the usefulness of and the procedure involved in hierarchical segmentation based on the CHAID algorithm as a multivariate analysis technique. A study carried out on leisure physicalsport behaviour of a university population served to facilitate an understanding of this method, the exhibition of its use and the interpretation of this technique. The study aimed to define the profiles of university students according to different degree of satisfaction with their physical-sport practices, as well as verify the existence of predictors of that satisfaction when all of them are related to one other. The article demonstrates the valuable capacity of this hierarchical segmentation technique in predicting and explaining certain behaviours, as well as determining the cause-effect relationship of these behaviours

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Contributions to comprehensible classification

xxx, 240 p.La tesis doctoral descrita en esta memoria ha contribuido a la mejora de dos tipos de algoritmos declasificación comprensibles: algoritmos de \'arboles de decisión consolidados y algoritmos de inducciónde reglas tipo PART.En cuanto a las contribuciones a la consolidación de algoritmos de árboles de decisión, se hapropuesto una nueva estrategia de remuestreo que ajusta el número de submuestras para permitir cambiarla distribución de clases en las submuestras sin perder información. Utilizando esta estrategia, la versiónconsolidada de C4.5 (CTC) obtiene mejores resultados que un amplio conjunto de algoritmoscomprensibles basados en algoritmos genéticos y clásicos. Tres nuevos algoritmos han sido consolidados:una variante de CHAID (CHAID*) y las versiones Probability Estimation Tree de C4.5 y CHAID* (C4.4y CHAIC). Todos los algoritmos consolidados obtienen mejores resultados que sus algoritmos de\'arboles de decisión base, con tres algoritmos consolidados clasificándose entre los cuatro mejores en unacomparativa. Finalmente, se ha analizado el efecto de la poda en algoritmos simples y consolidados de\'arboles de decisión, y se ha concluido que la estrategia de poda propuesta en esta tesis es la que obtiene mejores resultados.En cuanto a las contribuciones a algoritmos tipo PART de inducción de reglas, una primerapropuesta cambia varios aspectos de como PART genera \'arboles parciales y extrae reglas de estos, locual resulta en clasificadores con mejor capacidad de generalizar y menor complejidad estructuralcomparando con los generados por PART. Una segunda propuesta utiliza \'arboles completamentedesarrollados, en vez de parcialmente desarrollados, y genera conjuntos de reglas que obtienen aúnmejores resultados de clasificación y una complejidad estructural menor. Estas dos nuevas propuestas y elalgoritmo PART original han sido complementadas con variantes basadas en CHAID* para observar siestos beneficios pueden ser trasladados a otros algoritmos de \'arboles de decisión y se ha observado, dehecho, que los algoritmos tipo PART basados en CHAID* también crean clasificadores más simples ycon mejor capacidad de clasificar que CHAID

Contributions to comprehensible classification

xxx, 240 p.La tesis doctoral descrita en esta memoria ha contribuido a la mejora de dos tipos de algoritmos declasificación comprensibles: algoritmos de \'arboles de decisión consolidados y algoritmos de inducciónde reglas tipo PART.En cuanto a las contribuciones a la consolidación de algoritmos de árboles de decisión, se hapropuesto una nueva estrategia de remuestreo que ajusta el número de submuestras para permitir cambiarla distribución de clases en las submuestras sin perder información. Utilizando esta estrategia, la versiónconsolidada de C4.5 (CTC) obtiene mejores resultados que un amplio conjunto de algoritmoscomprensibles basados en algoritmos genéticos y clásicos. Tres nuevos algoritmos han sido consolidados:una variante de CHAID (CHAID*) y las versiones Probability Estimation Tree de C4.5 y CHAID* (C4.4y CHAIC). Todos los algoritmos consolidados obtienen mejores resultados que sus algoritmos de\'arboles de decisión base, con tres algoritmos consolidados clasificándose entre los cuatro mejores en unacomparativa. Finalmente, se ha analizado el efecto de la poda en algoritmos simples y consolidados de\'arboles de decisión, y se ha concluido que la estrategia de poda propuesta en esta tesis es la que obtiene mejores resultados.En cuanto a las contribuciones a algoritmos tipo PART de inducción de reglas, una primerapropuesta cambia varios aspectos de como PART genera \'arboles parciales y extrae reglas de estos, locual resulta en clasificadores con mejor capacidad de generalizar y menor complejidad estructuralcomparando con los generados por PART. Una segunda propuesta utiliza \'arboles completamentedesarrollados, en vez de parcialmente desarrollados, y genera conjuntos de reglas que obtienen aúnmejores resultados de clasificación y una complejidad estructural menor. Estas dos nuevas propuestas y elalgoritmo PART original han sido complementadas con variantes basadas en CHAID* para observar siestos beneficios pueden ser trasladados a otros algoritmos de \'arboles de decisión y se ha observado, dehecho, que los algoritmos tipo PART basados en CHAID* también crean clasificadores más simples ycon mejor capacidad de clasificar que CHAID

Effective techniques for handling incomplete data using decision trees

Decision Trees (DTs) have been recognized as one of the most successful formalisms for knowledge representation and reasoning and are currently applied to a variety of data mining or knowledge discovery applications, particularly for classification problems. There are several efficient methods to learn a DT from data. However, these methods are often limited to the assumption that data are complete. In this thesis, some contributions to the field of machine learning and statistics that solve the problem of extracting DTs for learning and classification tasks from incomplete databases are presented. The methodology underlying the thesis blends together well-established statistical theories with the most advanced techniques for machine learning and automated reasoning with uncertainty. The first contribution is the extensive simulations which study the impact of missing data on predictive accuracy of existing DTs which can cope with missing values, when missing values are in both the training and test sets or when they are in either of the two sets. All simulations are performed under missing completely at random, missing at random and informatively missing mechanisms and for different missing data patterns and proportions. The proposal of a simple, novel, yet effective proposed procedure for training and testing using decision trees in the presence of missing data is the next contribution. Original and simple splitting criteria for attribute selection in tree building are put forward. The proposed technique is evaluated and validated in empirical tests over many real world application domains. In this work, the proposed algorithm maintains (sometimes exceeds) the outstanding accuracy of multiple imputation, especially on datasets containing mixed attributes and purely nominal attributes. Also, the proposed algorithm greatly improves in accuracy for IM data. Another major advantage of this method over multiple imputation is the important saving in computational resources due to it simplicity. The next contribution is the proposal of three versions of simple probabilistic techniques that could be used for classifying incomplete vectors using decision trees based on complete data. The proposed procedure is superficially similar to that of fractional cases but more effective. The experimental results demonstrate that these approaches can achieve comparative quality to sophisticated algorithms like multiple imputation and therefore are applicable to all kinds of datasets. Finally, novel uses of two proposed ensemble procedures for handling incomplete training and test data are proposed and discussed. The algorithms combine the two best approaches either with resampling (REMIMIA) or without resampling (EMIMIA) of the training data before growing the decision trees. Experiments are used to evaluate and validate the success of the proposed ensemble methods with respect to individual missing data techniques in the form of empirical tests. EMIMIA attains the highest overall level of prediction accuracy

On-the-fly synthesizer programming with rule learning

This manuscript explores automatic programming of sound synthesis algorithms within the context of the performative artistic practice known as live coding. Writing source code in an improvised way to create music or visuals became an instrument the moment affordable computers were able to perform real-time sound synthesis with languages that keep their interpreter running. Ever since, live coding has dealt with real time programming of synthesis algorithms. For that purpose, one possibility is an algorithm that automatically creates variations out of a few presets selected by the user. However, the need for real-time feedback and the small size of the data sets (which can even be collected mid-performance) are constraints that make existing automatic sound synthesizer programmers and learning algorithms unfeasible. Also, the design of such algorithms is not oriented to create variations of a sound but rather to find the synthesizer parameters that match a given one. Other approaches create representations of the space of possible sounds, allowing the user to explore it by means of interactive evolution. Even though these systems are exploratory-oriented, they require longer run-times. This thesis investigates inductive rule learning for on-the-fly synthesizer programming. This approach is conceptually different from those found in both synthesizer programming and live coding literature. Rule models offer interpretability and allow working with the parameter values of the synthesis algorithms (even with symbolic data), making preprocessing unnecessary. RuLer, the proposed learning algorithm, receives a dataset containing user labeled combinations of parameter values of a synthesis algorithm. Among those combinations sharing the same label, it analyses the patterns based on dissimilarity. These patterns are described as an IF-THEN rule model. The algorithm parameters provide control to define what is considered a pattern. As patterns are the base for inducting new parameter settings, the algorithm parameters control the degree of consistency of the inducted settings respect to the original input data. An algorithm (named FuzzyRuLer) able to extend IF-THEN rules to hyperrectangles, which in turn are used as the cores of membership functions, is presented. The resulting fuzzy rule model creates a map of the entire input feature space. For such a pursuit, the algorithm generalizes the logical rules solving the contradictions by following a maximum volume heuristics. Across the manuscript it is discussed how, when machine learning algorithms are used as creative tools, glitches, errors or inaccuracies produced by the resulting models are sometimes desirable as they might offer novel, unpredictable results. The evaluation of the algorithms follows two paths. The first focuses on user tests. The second responds to the fact that this work was carried out within the computer science department and is intended to provide a broader, nonspecific domain evaluation of the algorithms performance using extrinsic benchmarks (i.e not belonging to a synthesizer's domain) for cross validation and minority oversampling. In oversampling tasks, using imbalanced datasets, the algorithm yields state-of-the-art results. Moreover, the synthetic points produced are significantly different from those created by the other algorithms and perform (controlled) exploration of more distant regions. Finally, accompanying the research, various performances, concerts and an album were produced with the algorithms and examples of this thesis. The reviews received and collections where the album has been featured show a positive reception within the community. Together, these evaluations suggest that rule learning is both an effective method and a promising path for further research.Aquest manuscrit explora la programació automàtica d’algorismes de síntesi de so dins del context de la pràctica artística performativa coneguda com a live coding. L'escriptura improvisada de codi font per crear música o visuals es va convertir en un instrument en el moment en què els ordinadors van poder realitzar síntesis de so en temps real amb llenguatges que mantenien el seu intèrpret en funcionament. D'aleshores ençà, el live coding comporta la programació en temps real d’algorismes de síntesi de so. Per a aquest propòsit, una possibilitat és tenir un algorisme que creï automàticament variacions a partir d'alguns presets seleccionats. No obstant, la necessitat de retroalimentació en temps real i la petita mida dels conjunts de dades són restriccions que fan que els programadors automàtics de sintetitzadors de so i els algorismes d’aprenentatge no siguin factibles d’utilitzar. A més, el seu disseny no està orientat a crear variacions d'un so, sinó a trobar els paràmetres del sintetitzador que aplicats a l'algorisme de síntesi produeixen un so determinat (target). Altres enfocaments creen representacions de l'espai de sons possibles, per permetre a l'usuari explorar-lo mitjançant l'evolució interactiva, però requereixen temps més llargs. Aquesta tesi investiga l'aprenentatge inductiu de regles per a la programació on-the-fly de sintetitzadors. Aquest enfocament és conceptualment diferent dels que es troben a la literatura. Els models de regles ofereixen interpretabilitat i permeten treballar amb els valors dels paràmetres dels algorismes de síntesi, sense processament previ. RuLer, l'algorisme d'aprenentatge proposat, rep dades amb combinacions etiquetades per l'usuari dels valors dels paràmetres d'un algorisme de síntesi. A continuació, analitza els patrons, basats en la dissimilitud, entre les combinacions de cada etiqueta. Aquests patrons es descriuen com un model de regles IF-THEN. Els paràmetres de l'algorisme proporcionen control per definir el que es considera un patró. Llavors, controlen el grau de consistència dels nous paràmetres de síntesi induïts respecte a les dades d'entrada originals. A continuació, es presenta un algorisme (FuzzyRuLer) capaç d’estendre les regles IF-THEN a hiperrectangles, que al seu torn s’utilitzen com a nuclis de funcions de pertinença. El model de regles difuses resultant crea un mapa complet de l'espai de la funció d'entrada. Per això, l'algorisme generalitza les regles lògiques seguint una heurística de volum màxim. Al llarg del manuscrit es discuteix com, quan s’utilitzen algorismes d’aprenentatge automàtic com a eines creatives, de vegades són desitjables glitches, errors o imprecisions produïdes pels models resultants, ja que poden oferir nous resultats imprevisibles. L'avaluació dels algorismes segueix dos camins. El primer es centra en proves d'usuari. El segon, que respon al fet que aquest treball es va dur a terme dins del departament de ciències de la computació, pretén proporcionar una avaluació més àmplia, no específica d'un domini, del rendiment dels algorismes mitjançant benchmarks extrínsecs utilitzats per cross-validation i minority oversampling. En tasques d'oversampling, mitjançant imbalanced data sets, l'algorisme proporciona resultats equiparables als de l'estat de l'art. A més, els punts sintètics produïts són significativament diferents als creats pels altres algorismes i realitzen exploracions (controlades) de regions més llunyanesEste manuscrito explora la programación automática de algoritmos de síntesis de sonido dentro del contexto de la práctica artística performativa conocida como live coding. La escritura de código fuente de forma improvisada para crear música o imágenes, se convirtió en un instrumento en el momento en que las computadoras asequibles pudieron realizar síntesis de sonido en tiempo real con lenguajes que mantuvieron su interprete en funcionamiento. Desde entonces, el live coding ha implicado la programación en tiempo real de algoritmos de síntesis. Para ese propósito, una posibilidad es tener un algoritmo que cree automáticamente variaciones a partir de unos pocos presets seleccionados. Sin embargo, la necesidad de retroalimentación en tiempo real y el pequeño tamaño de los conjuntos de datos (que incluso pueden recopilarse durante la misma actuación), limitan el uso de los algoritmos existentes, tanto de programación automática de sintetizadores como de aprendizaje de máquina. Además, el diseño de dichos algoritmos no está orientado a crear variaciones de un sonido, sino a encontrar los parámetros del sintetizador que coincidan con un sonido dado. Otros enfoques crean representaciones del espacio de posibles sonidos, para permitir al usuario explorarlo mediante evolución interactiva. Aunque estos sistemas están orientados a la exploración, requieren tiempos más largos. Esta tesis investiga el aprendizaje inductivo de reglas para la programación de sintetizadores on-the-fly. Este enfoque es conceptualmente diferente de los que se encuentran en la literatura, tanto de programación de sintetizadores como de live coding. Los modelos de reglas ofrecen interpretabilidad y permiten trabajar con los valores de los parámetros de los algoritmos de síntesis (incluso con datos simbólicos), haciendo innecesario el preprocesamiento. RuLer, el algoritmo de aprendizaje propuesto, recibe un conjunto de datos que contiene combinaciones, etiquetadas por el usuario, de valores de parámetros de un algoritmo de síntesis. Luego, analiza los patrones, en función de la disimilitud, entre las combinaciones de cada etiqueta. Estos patrones se describen como un modelo de reglas lógicas IF-THEN. Los parámetros del algoritmo proporcionan el control para definir qué se considera un patrón. Como los patrones son la base para inducir nuevas configuraciones de parámetros, los parámetros del algoritmo controlan también el grado de consistencia de las configuraciones inducidas con respecto a los datos de entrada originales. Luego, se presenta un algoritmo (llamado FuzzyRuLer) capaz de extender las reglas lógicas tipo IF-THEN a hiperrectángulos, que a su vez se utilizan como núcleos de funciones de pertenencia. El modelo de reglas difusas resultante crea un mapa completo del espacio de las clases de entrada. Para tal fin, el algoritmo generaliza las reglas lógicas resolviendo las contradicciones utilizando una heurística de máximo volumen. A lo largo del manuscrito se analiza cómo, cuando los algoritmos de aprendizaje automático se utilizan como herramientas creativas, los glitches, errores o inexactitudes producidas por los modelos resultantes son a veces deseables, ya que pueden ofrecer resultados novedosos e impredecibles. La evaluación de los algoritmos sigue dos caminos. El primero se centra en pruebas de usuario. El segundo, responde al hecho de que este trabajo se llevó a cabo dentro del departamento de ciencias de la computación y está destinado a proporcionar una evaluación más amplia, no de dominio específica, del rendimiento de los algoritmos utilizando beanchmarks extrínsecos para cross-validation y oversampling. En estas últimas pruebas, utilizando conjuntos de datos no balanceados, el algoritmo produce resultados equiparables a los del estado del arte. Además, los puntos sintéticos producidos son significativamente diferentes de los creados por los otros algoritmos y realizan una exploración (controlada) de regiones más distantes. Finalmente, acompañando la investigación, realicé diversas presentaciones, conciertos y un ´álbum utilizando los algoritmos y ejemplos de esta tesis. Las críticas recibidas y las listas donde se ha presentado el álbum muestran una recepción positiva de la comunidad. En conjunto, estas evaluaciones sugieren que el aprendizaje de reglas es al mismo tiempo un método eficaz y un camino prometedor para futuras investigaciones.Postprint (published version

Household activity-travel behavior : implementation of within-household interactions

Although the importance of households as a decision making unit has been recognized in seminal work in activity-based analysis of transport demand, most comprehensive models have relied on individual activity-travel patterns. The transformation of these models to household level models and the explicit consideration of resource allocation, task allocation and joint activity participation decisions is thus a challenge and research frontier in this field of study. To contribute to this expanding field, the aim of this PhD study is to develop such an activity-based model. More specifically, the slightly ad hoc treatment of household decisions in the ALBATROSS model is replaced with a systematic incorporation of household decisions. The new variant, based on the MON 2004 data, is compared in terms of goodness-of-fit and sensitivity with the previous version of the model. To this end, the thesis is organized as follows. Chapter 2 provides a review of past research efforts concerning the determinants of household decision making. We discuss how household decision making has been treated in comprehensive activity-based models of transport demand. This line of research started with analytical studies on household decision making taking into account car allocation and usage decisions. Further literatures addressed task and time allocation decisions. They found that household types, defined by the number of household heads and work status, strongly influence activity time allocation and trip chaining. The presence of children in the household has a positive effect on the duration of all out-of-home activities in household trip chaining, except for the duration of out-of-home discretionary activities of households having children under 5 years old. This suggests that the presence of children induces more chaining of trips and more time allocated to these trip chains. Households having more children of 16 years of age and over are more likely to spend time in trip chaining for out-of-home subsistence activities. Finally, they found that flexible work arrangements tend to be correlated with less trip chaining for the work trip. In addition to these studies, there is also a literature on joint activity participation. Several studies have examined the effect of household attributes on joint activity-travel behavior. They found that joint activities involving household heads are significantly affected by the presence of children. Couples without children living at home are more likely to pursue joint out-of-home non-work activities than couples with children. In households with children, most joint activities between adults are at home. In addition, the employment status of the household heads influences whether a joint activity originates from home or from an out-of-home contact point. In additional to analytical studies, existing comprehensive activity-based models are reviewed in this chapter in terms of their inclusion and treatment of household decisions. Comprehensive in this context means that the model allows predicting a combination of choice facets, at least compatible with those underlying traditional four-step models: i.e. activity generation, destination and transport mode choice. The discussion is restricted to fully operational models. Over the years, many activity-based models have been suggested in the literature, including constraints-based models, micro-simulation models, (nested logit) utility-maximizing models, suites of advanced statistical models and rule-based models. Most of these models either do not incorporate household decisions at all, or only in a limited way. Chapter 3 discusses the conceptual framework of this thesis for modeling household activity-travel behavior. Because the thesis is an attempt of elaborating the ALBATROSS model, we discuss this model in more detail, including its conceptual framework. Further, we explain the entire process underlying the ALBATROSS system and the inclusion of household decision making in the process, such as joint participation, activity allocation, car allocation for non-work tours, and some other choice facets. Household decision making is mostly applicable to non-work activities, but the problem of car allocation is highly relevant for work tours in car-deficient households. Further, we summarize the methodology that was used in this study: decision tree induction using a CHAID-based induction algorithm being the core method of ALBATROSS. The remaining chapters then present the results of the various derived decision tress for the sequential choice facets that together make up the ALBATROSS model. Chapter 4 describes the results for car allocation choice focusing on work activities. In this analysis work-tours as opposed to work trips are considered. The car allocation model focuses on car-deficient households (i.e., more drivers than cars present) and a joint decision between the two heads (mostly, a female and male). We also assume that both male-female are drivers and at least one of them has a work activity on the day considered. Furthermore, the model includes the option that none of the household heads uses the car, but some other means of transport. The results show that the propensity of men driving a car to the work place is higher than that of women, particularly, when women have no work activity or women’s work place is in the same zone as the home location. This finding is consistent with the common notion that women use a slow or public transport mode more often to travel to activity locations. Women tend to use the car when men have no work activities or men work at home. Chapter 5 reports the empirical derivation of a household decision model of activity choice taking into account joint participation and task allocation between household heads. These are considered household-level decisions given that they involve commitments of multiple persons, in particular the two-head households. Of the 10 activity categories concerned, 7 activity categories (non-work activities) are used in this study, i.e. bring/get, shopping to 1 store, shopping to multiple store, service-related, social, leisure, and touring. The first four activities are deemed task allocation activities and the rest are non-task activities (discretionary). Hence, two decision trees were derived from diary data. The activity participation model, given the large number of observations that could be derived from the data, included more than 300 condition action rules. The household task allocation model also involved an extensive set of decision rules, involving more than 90 condition-action rules. In both cases, the validity of the decision tree is satisfactory in the sense that the derived rules are readily interpretable and the overall goodness-of-fit of the model on a validation set is acceptable as well. Chapter 6 focuses on the joint participation of male-female heads in non-work activities and attempts to model the timing and duration decisions for these activities, using decision tree induction. Decision tree results indicated that there were 17 and 31condition-action rules derived for the duration model and start time model, respectively. The improvement in S-value (a measure of prediction accuracy) relative to a null model as well as an F-statistic indicates that there is a moderately strong association between condition variables at household, individual, activity and schedule level, on the one hand, and the decision, on the other. The S-value shows a more substantial improvement in the start-time model compared to the duration model. The results show that activity type has the most significant influence in both models. In addition, time availability for non-work activities during morning off-peak periods has a strong influence on start time decisions. The results also suggest that there is a substantial influence of duration decisions on start time decisions. Joint participation of household members in activities tends to lead to longer activity duration and earlier start times. Overall, modeling timing and duration of joint activity participation decisions at the household level proves to have some clear advantages. Chapter 7 discusses the development of the household location choice model taking into account the independent and joint activities, in particular non-work activities. In ALBATROSS, location choice is modeled for independent and joint activity participation of the household heads based on the concept of detour time. The detour time of a candidate location for an activity is defined as the extra travel time required to implement the activity in the context of the current activity schedule. There were two decision tree models for both independent and joint activity categories. The first model relates to the decision whether or not the activity is performed at the same location as the previous activity, whether the activity is done at the same location as the next activity, or whether it is conducted elsewhere. The second model relates to the last choice option in the first model and comprises 25 choice alternatives. It verifies the location in terms of a combination of size - distance classes. The size class depends on a particular activity type and the size of available facilities at the activity location. Size is classified into 5 categories based on employment in the relevant sector for the activity considered and distance is classified in terms of a detour travel time (by car) also into 5 categories. The tendency of conducting a particular activity at the same location as the previous activity is higher for independent activities than for joint activities. The same condition also applies to activities that are conducted at the same location as the next activity. These results imply that males and females are more likely to conduct multiple activities at one particular location independently than jointly. These results do make sense, since the activity-travel behavior of one person is different from the other person, even though male-female couples live in the same household. Chapter 8 is concerned with car allocation behavior for non-work activities. In this study, the assumption is similar to the assumption in Chapter 4 where tours are taken into account instead of trips. Travel for any activity episode or set of chained activity episodes that does not include a work activity is considered a non-work tour. The problem of modeling this allocation problem for non-work tours is more complex than for work tours because the decision at this stage depends considerably on the outcome of the previous stages in the scheduling process. Hence, the car can be allocated to male, female or none. Further, only overlapping non-work activities of the male’s and female that occur in the same time slot are taken into account. Overlapping tours are defined as a pair of tours conducted by respectively male and female of which the start and/or end times of each tour (simulating use of a car for the tour) defines a fully or partially overlapping episode. As a tour consists of a sequence of trips that starts and ends at a particular location (i.e., home), the primary activity in each tour needs to be determined. In order to identify the primary activity in a particular tour, we consider a hierarchical order of activity priority. In particular, 10 activity categories are considered in order of priority starting from work, business and other (mandatory)activities. A group of non-work activities is considered, such as escorting, shopping (daily and non-daily), service-related, social, leisure, and touring. Since business and other mandatory activities are not considered primary work activities, they are not dealt with in the first stage of the scheduling process and, hence, they are also considered as non-work activities in this model. The results show a satisfactory improvement in goodness-of-fit of the decision tree model compared to the null model. Gender seems to play an important role. A descriptive analysis indicates that men more often than women get the car for nonwork tours for which a car allocation decision needs to be made. Tour-level attributes are shown to influence the household car allocation decision for non-work tours. The decision to allocate the car is considerably influenced by the longest distance (travel time) from home to a particular location in a tour of men and women. The probability that the men and women get the car monotonically increases with increasing travel time. Socio-economic and situational factors have less influence on the car allocation decision. Overall, men have more influence on the car allocation decision for non-work tours, as indicated by the number of influential variables that relates to the males in the impact table. Chapter 9 discusses the results of the integrated model of ALBATROSS. In order to test the performance of the ALBATROSS system based on all decision tables for the assumed scheduling process, the validity and sensitivity of the integrated model were evaluated and compared with the performance of the old model. First, the validity of the model versions was compared by evaluating the extent to which the model is able to reproduce observed frequency distributions and mobility indicators in the MON dataset. In that sense, no major differences were expected. Instead, it was expected that the new model is able to reproduce the aggregated distributions as well as the existing model. A Second effort was to examine the sensitivity of the models by applying the models to a particular scenario of change in the Dutch population. It was expected that the new model was more sensitive to such scenarios. The scenario assumed an increase of 41 % in labor participation of women household heads (labor scenario) assuming the year 2000 as the base year. A fraction of 10% of the Dutch population in the year 2000 was generated using the synthesis module of ALBATROSS for the baseline and the labor scenario. As expected, in the context of validity test, the new model showed equal or slightly better goodness-of-fit for most choice facets, except for time of day and trip-chaining. The new model proved to be more sensitive to facets such as activity type, start time, trip-chaining, location, etc., in response to scenarios change. In particular, the new model predicted somewhat different responses that could be interpreted in terms of the better representation of opportunities and requirements related to task allocation and joint activity participation. In sum, by considering decisions of household heads in interaction, the system is able to predict with increased sensitivity activity-travel rescheduling processes of households in response to change

Formal Linguistic Models and Knowledge Processing. A Structuralist Approach to Rule-Based Ontology Learning and Population

2013 - 2014The main aim of this research is to propose a structuralist approach for knowledge processing by means of ontology learning and population, achieved starting from unstructured and structured texts. The method suggested includes distributional semantic approaches and NL formalization theories, in order to develop a framework, which relies upon deep linguistic analysis... [edited by author]XIII n.s