Penguins Search Optimisation Algorithm for Association Rules Mining

Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decision-making process. All existing ARM algorithms are time consuming and generate a very large number of association rules with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different datasets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well-known ARM algorithms in both execution time and solution quality

Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features

A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

Meta-RaPS Hybridization with Machine Learning Algorithms

This dissertation focuses on advancing the Metaheuristic for Randomized Priority Search algorithm, known as Meta-RaPS, by integrating it with machine learning algorithms. Introducing a new metaheuristic algorithm starts with demonstrating its performance. This is accomplished by using the new algorithm to solve various combinatorial optimization problems in their basic form. The next stage focuses on advancing the new algorithm by strengthening its relatively weaker characteristics. In the third traditional stage, the algorithms are exercised in solving more complex optimization problems. In the case of effective algorithms, the second and third stages can occur in parallel as researchers are eager to employ good algorithms to solve complex problems. The third stage can inadvertently strengthen the original algorithm. The simplicity and effectiveness Meta-RaPS enjoys places it in both second and third research stages concurrently. This dissertation explores strengthening Meta-RaPS by incorporating memory and learning features. The major conceptual frameworks that guided this work are the Adaptive Memory Programming framework (or AMP) and the metaheuristic hybridization taxonomy. The concepts from both frameworks are followed when identifying useful information that Meta-RaPS can collect during execution. Hybridizing Meta-RaPS with machine learning algorithms helped in transforming the collected information into knowledge. The learning concepts selected are supervised and unsupervised learning. The algorithms selected to achieve both types of learning are the Inductive Decision Tree (supervised learning) and Association Rules (unsupervised learning). The objective behind hybridizing Meta-RaPS with an Inductive Decision Tree algorithm is to perform online control for Meta-RaPS\u27 parameters. This Inductive Decision Tree algorithm is used to find favorable parameter values using knowledge gained from previous Meta-RaPS iterations. The values selected are used in future Meta-RaPS iterations. The objective behind hybridizing Meta-RaPS with an Association Rules algorithm is to identify patterns associated with good solutions. These patterns are considered knowledge and are inherited as starting points for in future Meta-RaPS iteration. The performance of the hybrid Meta-RaPS algorithms is demonstrated by solving the capacitated Vehicle Routing Problem with and without time windows

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Discovering Higher-order SNP Interactions in High-dimensional Genomic Data

In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise

Nuevos retos en clasificación asociativa: Big Data y aplicaciones

La clasificación asociativa surge como resultado de la unión de dos importantes ámbitos del aprendizaje automático. Por un lado la tarea descriptiva de extracción de reglas de asociación, como mecanismo para obtener información previamente desconocida e interesante de un conjunto de datos, combinado con una tarea predictiva, como es la clasificación, que permite en base a un conjunto de variables explicativas y previamente conocidas realizar una predicción sobre una variable de interés o predictiva. Los objetivos de esta tesis doctoral son los siguientes: 1) El estudio y el análisis del estado del arte de tanto la extracción de reglas de asociación como de la clasificación asociativa; 2) La propuesta de nuevos modelos de clasificación asociativa así como de extracción de reglas de asociación teniendo en cuenta la obtención de modelos que sean precisos, interpretables, eficientes así como flexibles para poder introducir conocimiento subjetivo en éstos. 3) Adicionalmente, y dado la gran cantidad de datos que cada día se genera en las últimas décadas, se prestará especial atención al tratamiento de grandes cantidades datos, también conocido como Big Data. En primer lugar, se ha analizado el estado del arte tanto de clasificación asociativa como de la extracción de reglas de asociación. En este sentido, se ha realizado un estudio y análisis exhaustivo de la bibliografía de los trabajos relacionados para poder conocer con gran nivel de detalle el estado del arte. Como resultado, se ha permitido sentar las bases para la consecución de los demás objetivos así como detectar que dentro de la clasificación asociativa se requería de algún mecanismo que facilitara la unificación de comparativas así como que fueran lo más completas posibles. Para tal fin, se ha propuesto una herramienta de software que cuenta con al menos un algoritmo de todas las categorías que componen la taxonomía actual. Esto permitirá dentro de las investigaciones del área, realizar comparaciones más diversas y completas que hasta el momento se consideraba una tarea en el mejor de los casos muy ardua, al no estar disponibles muchos de los algoritmos en un formato ejecutable ni mucho menos como código abierto. Además, esta herramienta también dispone de un conjunto muy diverso de métricas que permite cuantificar la calidad de los resultados desde diferentes perspectivas. Esto permite conseguir clasificadores lo más completos posibles, así como para unificar futuras comparaciones con otras propuestas. En segundo lugar, y como resultado del análisis previo, se ha detectado que las propuestas actuales no permiten escalar, ni horizontalmente, ni verticalmente, las metodologías sobre conjuntos de datos relativamente grandes. Dado el creciente interés, tanto del mundo académico como del industrial, de aumentar la capacidad de cómputo a ingentes cantidades de datos, se ha considerado interesante continuar esta tesis doctoral realizando un análisis de diferentes propuestas sobre Big Data. Para tal fin, se ha comenzado realizando un análisis pormenorizado de los últimos avances para el tratamiento de tal cantidad de datos. En este respecto, se ha prestado especial atención a la computación distribuida ya que ha demostrado ser el único procedimiento que permite el tratamiento de grandes cantidades de datos sin la realización de técnicas de muestreo. En concreto, se ha prestado especial atención a las metodologías basadas en MapReduce que permite la descomposición de problemas complejos en fracciones divisibles y paralelizables, que posteriormente pueden ser agrupadas para obtener el resultado final. Como resultado de este objetivo se han propuesto diferentes algoritmos que permiten el tratamiento de grandes cantidades de datos, sin la pérdida de precisión ni interpretabilidad. Todos los algoritmos propuestos se han diseñado para que puedan funcionar sobre las implementaciones de código abierto más conocidas de MapReduce. En tercer y último lugar, se ha considerado interesante realizar una propuesta que mejore el estado del arte de la clasificación asociativa. Para tal fin, y dado que las reglas de asociación son la base y factores determinantes para los clasificadores asociativos, se ha comenzado realizando una nueva propuesta para la extracción de reglas de asociación. En este aspecto, se ha combinado el uso de los últimos avances en computación distribuida, como MapReduce, con los algoritmos evolutivos que han demostrado obtener excelentes resultados en el área. En particular, se ha hecho uso de programación genética gramatical por su flexibilidad para codificar las soluciones, así como introducir conocimiento subjetivo en el proceso de búsqueda a la vez que permiten aliviar los requisitos computacionales y de memoria. Este nuevo algoritmo, supone una mejora significativa de la extracción de reglas de asociación ya que ha demostrado obtener mejores resultados que las propuestas existentes sobre diferentes tipos de datos así como sobre diferentes métricas de interés, es decir, no sólo obtiene mejores resultados sobre Big Data, sino que se ha comparado en su versión secuencial con los algoritmos existentes. Una vez que se ha conseguido este algoritmo que permite extraer excelentes reglas de asociación, se ha adaptado para la obtención de reglas de asociación de clase así como para obtener un clasificador a partir de tales reglas. De nuevo, se ha hecho uso de programación genética gramatical para la obtención del clasificador de forma que se permite al usuario no sólo introducir conocimiento subjetivo en las propias formas de las reglas, sino también en la forma final del clasificador. Esta nueva propuesta también se ha comparado con los algoritmos existentes de clasificación asociativa forma secuencial para garantizar que consigue diferencias significativas respecto a éstos en términos de exactitud, interpretabilidad y eficiencia. Adicionalmente, también se ha comparado con otras propuestas específicas de Big Data demostrado obtener excelentes resultados a la vez que mantiene un compromiso entre los objetivos conflictivos de interpretabilidad, exactitud y eficiencia. Esta tesis doctoral se ha desarrollado bajo un entorno experimental apropiado, haciendo uso de diversos conjunto de datos incluyendo tanto datos de pequeña dimensionalidad como Big Data. Además, todos los conjuntos de datos usados están publicados libremente y conforman un conglomerado de diversas dimensionalidades, número de instancias y de clases. Todos los resultados obtenidos se han comparado con el estado de arte correspondiente, y se ha hecho uso de tests estadísticos no paramétricos para comprobar que las diferencias encontradas son significativas desde un punto de vista estadístico, y no son fruto del azar. Adicionalmente, todas las comparaciones realizadas consideran diferentes perspectivas, es decir, se ha analizado rendimiento, eficiencia, precisión así como interpretabilidad en cada uno de los estudios.This Doctoral Thesis aims at solving the challenging problem of associative classification and its application on very large datasets. First, associative classification state-of-art has been studied and analyzed, and a new tool covering the whole taxonomy of algorithms as well as providing many different measures has been proposed. The goal of this tool is two-fold: 1) unification of comparisons, since existing works compare with very different measures; 2) providing a unique tool which has at least one algorithm of each category forming the taxonomy. This tool is a very important advancement in the field, since until the moment the whole taxonomy has not been covered due to that many algorithms have not been released as open source nor they were available to be run. Second, AC has been analyzed on very large quantities of data. In this regard, many different platforms for distributed computing have been studied and different proposals have been developed on them. These proposals enable to deal with very large data in a efficient way scaling up the load on very different compute nodes. Third, as one of the most important part of the associative classification is to extract high quality rules, it has been proposed a novel grammar-guided genetic programming algorithm which enables to obtain interesting association rules with regard to different metrics and in different kinds of data, including truly Big Data datasets. This proposal has proved to obtain very good results in terms of both quality and interpretability, at the same time of providing a very flexible way of representing the solutions and enabling to introduce subjective knowledge in the search process. Then, a novel algorithm has been proposed for associative classification using a non-trivial adaptation of the aforementioned algorithm to obtain the rules forming the classifier. This methodology is also based on grammar-guided genetic programming enabling user not only to constrain the form of the rules, but the final form of the classifier. Results have proved that this algorithm obtains very accurate classifiers at the same time of maintaining a good level of interpretability. All the methodologies proposed along this Thesis has been evaluated using a proper experimental framework, using a varied set of datasets including both classical and Big Data dataset, and analyzing different metrics to quantify the quality of the algorithms with regard to different perspectives. Results have been compared with state-of-the-art and they have been verified by means of non-parametric statistical tests proving that the proposed methods overcome to existing approaches

Predicate based association rules mining with new interestingness measure

Association Rule Mining (ARM) is one of the fundamental components in the field of data mining that discovers frequent itemsets and interesting relationships for predicting the associative and correlative behaviours for new data. However, traditional ARM techniques are based on support-confidence that discovers interesting association rules (ARs) using predefined minimum support (minsupp) and minimum confidence (minconf) threshold. In addition, traditional AR techniques only consider frequent items while ignoring rare ones. Thus, a new parameter-less predicated based ARM technique was proposed to address these limitations, which was enhanced to handle the frequent and rare items at the same time. Furthermore, a new interestingness measure, called g measure, was developed to select only highly interesting rules. In this proposed technique, interesting combinations were firstly selected by considering both the frequent and the rare items from a dataset. They were then mapped to the pseudo implications using predefined logical conditions. Later, inference rules were used to validate the pseudo-implications to discover rules within the set of mapped pseudo-implications. The resultant set of interesting rules was then referred to as the predicate based association rules. Zoo, breast cancer, and car evaluation datasets were used for conducting experiments. The results of the experiments were evaluated by its comparison with various classification techniques, traditional ARM technique and the coherent rule mining technique. The predicate-based rule mining approach gained an accuracy of 93.33%. In addition, the results of the g measure were compared with a state-of-the-art interestingness measure developed for a coherent rule mining technique called the h value. Predicate rules were discovered with an average confidence value of 0.754 for the zoo dataset and 0.949 for the breast cancer dataset, while the average confidence of the predicate rules found from the car evaluation dataset was 0.582. Results of this study showed that a set of interesting and highly reliable rules were discovered, including frequent, rare and negative association rules that have a higher confidence value. This research resulted in designing a methodology in rule mining which does not rely on the minsupp and minconf threshold. Also, a complete set of association rules are discovered by the proposed technique. Finally, the interestingness measure property for the selection of combinations from datasets makes it possible to reduce the exponential searching of the rules