Fuzzy-Citation-KNN: a fuzzy nearest neighbor approach for multi-instance classification

This contribution deals with multi-instance classification, where the labeled data samples are bags composed on instances instead of labeled instances as in standard classification. Every bag contains a number of traditional instances (described by a number of attributes) and the number of instances is not usually the same in all the bags. So, the whole bag is labeled but the instances that compose the bag are not individually labeled. We propose a fuzzy sets based extension of the well known algorithm called Citation-KNN, a reference method in multi-instance classification. Citation-KNN uses two types of examples in the classification rule: neighbors and citers of the bag to be classified. We analyze two versions of our proposal, one of them using both neighbors and citers, and the other one using only neighbors. Our approach uses the Hausdorff distance and it is based on the FuzzyKNN algorithm. Several data-sets from KEEL data-set repository are used in the experimental study and we compare our proposals with the original Citation-KNN algorithm

Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to?

Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the "4 W" questions are posed and addressed with the aim of understanding the current context of this topic and its significance. Specifically, it will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area. They must play an important role for the emerging area of eXplainable Artificial Intelligence (XAI) learning from data

Visualización de conjunto de datos de múltiples instancias

Este trabajo de grado aborda la problemática de la visualización de conjuntos de datos de múltiples instancias (MI), en busca de entender las particularidades de estos conjuntos de datos y sus relaciones. Como en la literatura existen pocos trabajos relacionados a este tema, se considera que el resultado puede ser de utilidad para quienes actualmente trabajan con el paradigma de aprendizaje de múltiples instancias (MIL). Así, la intención de este trabajo es desarrollar un método de visualización que permita a los usuarios entender cuáles son las relaciones o patrones ocultos en los conjuntos de datos de MI. Con este n se plantea una pregunta de investigación importante, Que métodos de visualización se pueden adaptar para explorar conjuntos de datos de MI. La respuesta a la pregunta de investigación se busca mediante la creación de una propuesta de visualización y experimentando con diferentes métodos de visualización en los conjuntos de datos. La propuesta de visualización se validó mediante encuestas y cuestionarios a expertos en MIL además con pruebas y comparaciones internas. Los experimentos realizados mostraron que usar métodos combinados de visualización permite extraer más información del conjunto de datos. Teniendo esto en cuenta y siguiendo las recomendaciones de los expertos, sería bueno crear herramientas que permitan representar un conjunto de MI en diferentes métodos de visualización y a su ve hacer herramientas más intuitivas, para que el proceso de visualización de datos sea más rápido y efectivo en la detección de patrones.This degree work addresses the problem of the visualization of data sets of multiple instances (MI), seeking to understand the particularities of these data sets and their relationships. As there are few works related to this topic in the literature, it is considered that the result may be useful for those who currently work with the multi-instance learning paradigm (MIL). Thus, the intention of this work is to develop a visualization method that allows users to understand what the relationships or hidden patterns in MI data sets. To this end, an important research question is posed, what visualization methods can be adapted to explore MI data sets? The answer to the research question is sought by creating a visualization proposal and experimenting with different visualization methods on the data sets. The visualization proposal was validated through surveys and questionnaires to MIL experts in addition to internal tests and comparisons. The experiments carried out showed that using combined visualization methods allows extracting more information from the data set. Taking this into account and following the recommendations of the experts, it would be good to create tools that allow representing a set of MI in different visualization methods and in turn make more intuitive tools, so that the data visualization process is faster and more effective in pattern detection.Magíster en Ingeniería de SoftwareMaestrí

Fuzzy multi-instance classifiers

Multi-instance learning is a setting in supervised learning where the data consist of bags of instances. Samples in the dataset are groups of individual instances. In classification problems, a decision value is assigned to the entire bag, and the classification of an unseen bag involves the prediction of the decision value based on the instances it contains. In this paper, we develop a framework for multi-instance classifiers based on fuzzy set theory. Fuzzy sets have been used in many machine learning applications, but so far not in the classification of multi-instance data. We explore its untapped potential here. We interpret the classes as fuzzy sets and determine membership degrees of unseen bags to these sets based on the available training data. In doing so, we develop a framework of classifiers that extract the required membership degrees either at the level of instances (instance-based) or at the level of bags (bag-based). We offer an extensive analysis of the different settings within the proposed framework. We experimentally compare our proposal to state-of-the-art multi-instance classifiers, and based on two evaluation measures, our methods are shown to perform very well