850 research outputs found
Data streams classification by incremental rule learning with parameterized generalization
Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper describes a classification system based on decision rules that may store up--to--date border examples to avoid unnecessary revisions when virtual drifts are present in data. Consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbor algorithm. In addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another
Incremental algorithm for Decision Rule generation in data stream contexts
Actualmente, la ciencia de datos está ganando mucha atención en diferentes sectores.
Concretamente en la industria, muchas aplicaciones pueden ser consideradas. Utilizar
técnicas de ciencia de datos en el proceso de toma de decisiones es una de esas
aplicaciones que pueden aportar valor a la industria. El incremento de la disponibilidad
de los datos y de la aparición de flujos continuos en forma de data streams hace
emerger nuevos retos a la hora de trabajar con datos cambiantes. Este trabajo presenta
una propuesta innovadora, Incremental Decision Rules Algorithm (IDRA), un
algoritmo que, de manera incremental, genera y modifica reglas de decisión para
entornos de data stream para incorporar cambios que puedan aparecer a lo largo del
tiempo. Este método busca proponer una nueva estructura de reglas que busca mejorar
el proceso de toma de decisiones, planteando una base de conocimiento descriptiva y
transparente que pueda ser integrada en una herramienta decisional. Esta tesis describe
la lógica existente bajo la propuesta de IDRA, en todas sus versiones, y propone una
variedad de experimentos para compararlas con un método clásico (CREA) y un
método adaptativo (VFDR). Conjuntos de datos reales, juntamente con algunos
escenarios simulados con diferentes tipos y ratios de error, se utilizan para comparar
estos algoritmos. El estudio prueba que IDRA, específicamente la versión reactiva de
IDRA (RIDRA), mejora la precisión de VFDR y CREA en todos los escenarios, tanto
reales como simulados, a cambio de un incremento en el tiempo.Nowadays, data science is earning a lot of attention in many different sectors.
Specifically in the industry, many applications might be considered. Using data
science techniques in the decision-making process is a valuable approach among the
mentioned applications. Along with this, the growth of data availability and the
appearance of continuous data flows in the form of data stream arise other challenges
when dealing with changing data. This work presents a novel proposal of an algorithm,
Incremental Decision Rules Algorithm (IDRA), that incrementally generates and
modify decision rules for data stream contexts to incorporate the changes that could
appear over time. This method aims to propose new rule structures that improve the
decision-making process by providing a descriptive and transparent base of knowledge
that could be integrated in a decision tool. This work describes the logic underneath
IDRA, in all its versions, and proposes a variety of experiments to compare them with
a classical method (CREA) and an adaptive method (VFDR). Some real datasets,
together with some simulated scenarios with different error types and rates are used to
compare these algorithms. The study proved that IDRA, specifically the reactive
version of IDRA (RIDRA), improves the accuracies of VFDR and CREA in all the
studied scenarios, both real and simulated, in exchange of more time
Manipulating ethos and pathos: Accents, product complexity, and promotional messages in Chile
This dissertation is motivated by fundamental questions about source effects in persuasive communications: Do receiver attributes influence perceptions about the source and about the object of the message? Do source and object cues influence receiver perceptions about the source? Do source and object cues influence receiver perceptions about the object of the message?
Traditional conceptions of receiver responses to a source have focused on character trait inferences. Of these character trait inferences, the literature on source credibility appears to converge on two categories: source expertise and source trustworthiness. A more recent stream of research has grown around the concept of homophily, a term coined by Lazarsfeld and Merton (1954) to refer to the tendency of individuals to associate with others similar to themselves.
This dissertation conceptualizes as social traits those source attributes associated with receiver perceptions of similarity between themselves and sources. This study tested whether inferences about the social and character traits of a source are separately significant predictors of overall assessments by receivers.
The study was conducted in central Chile. Respondents were classified according to socioeconomic background and asked to answer questions about their impressions of a recorded promotional message. Each of the 450 respondents in the study heard only one of six recorded messages. Individual messages promoted one of two products and were recorded in one of three local accents, corresponding to the socioeconomically differentiated neighborhoods in the community.
The results of the study imply affirmative answers to each of the basic questions guiding this research. Both social and character trait inferences are found to be significant predictors of overall assessments of source credibility. Of the traditional character traits, expertise is found to play a conditional rather than a permanent role in receiver evaluations of a message. Signals of source-receiver similarity, at least with respect to accent, may elicit unfavorable assessments from lower status receivers
Unsupervised learning for long-term autonomy
This thesis investigates methods to enable a robot to build and maintain an environment model in an automatic manner. Such capabilities are especially important in long-term autonomy, where robots operate for extended periods of time without human intervention. In such scenarios we can no longer assume that the environment and the models will remain static. Rather changes are expected and the robot needs to adapt to the new, unseen, circumstances automatically. The approach described in this thesis is based on clustering the robot’s sensing information. This provides a compact representation of the data which can be updated as more information becomes available. The work builds on affinity propagation (Frey and Dueck, 2007), a recent clustering method which obtains high quality clusters while only requiring similarities between pairs of points, and importantly, selecting the number of clusters automatically. This is essential for real autonomy as we typically do not know “a priori” how many clusters best represent the data. The contributions of this thesis a three fold. First a self-supervised method capable of learning a visual appearance model in long-term autonomy settings is presented. Secondly, affinity propagation is extended to handle multiple sensor modalities, often occurring in robotics, in a principle way. Third, a method for joint clustering and outlier selection is proposed which selects a user defined number of outlier while clustering the data. This is solved using an extension of affinity propagation as well as a Lagrangian duality approach which provides guarantees on the optimality of the solution
- …