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Sistemas classificadores evolutivos para problemas multirrótulo; Learning classifier system for multi-label classification

Vallim, Rosane Maria Maffei
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 27/07/2009 Português
Relevância na Pesquisa
86.28%
Classificação é, provavelmente, a tarefa mais estudada na área de Aprendizado de Máquina, possuindo aplicação em uma grande quantidade de problemas reais, como categorização de textos, diagnóstico médico, problemas de bioinformática, além de aplicações comerciais e industriais. De um modo geral, os problemas de classificação podem ser categorizados quanto ao número de rótulos de classe que podem ser associados à cada exemplo de entrada. A abordagem mais investigada pela comunidade de Aprendizado de Máquina é a de classes mutuamente exclusivas. Entretanto, existe uma grande variedade de problemas importantes em que cada exemplo de entrada pode ser associado a mais de um rótulo ou classe. Esses problemas são denominados problemas de classificação multirrótulo. Os Learning Classifier Systems(LCS) constituem uma técnica de Indução de Regras de Classificação que tem como principal mecanismo de busca um Algoritmo Genético. Essa técnica busca encontrar um conjunto de regras que tenha alta precisão de classificação, que seja compreensível e que possua regras consideradas interessantes sob o ponto de vista de classificação. Apesar de existirem na literatura diversos trabalhos sobre os LCS para problemas de classificação com classes mutuamente exclusivas...

Grid data mining by means of learning classifier systems and distributed model induction

Santos, Manuel Filipe; Mathew, Wesley; Santos, Henrique Dinis dos
Fonte: Universidade do Minho Publicador: Universidade do Minho
Tipo: Conferência ou Objeto de Conferência
Publicado em //2011 Português
Relevância na Pesquisa
86.19%
This paper introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Different methods of merging data mining models generated at different distributed sites are explored. Centralized Data Mining (CDM) is a conventional method of data mining in distributed data. In CDM, data that is stored in distributed locations have to be collected and stored in a central repository before executing the data mining algorithm. CDM method is reliable; however it is expensive (computational, communicational and implementation costs are high). Alternatively, Distributed Data Mining (DDM) approach is economical but it has limitations in combining local models. In DDM, the data mining algorithm has to be executed at each one of the sites to induce a local model. Those induced local models are collected and combined to form a global data mining model. In this work six different tactics are used for constructing the global model in DDM: Generalized Classifier Method (GCM); Specific Classifier Method (SCM); Weighed Classifier Method (WCM); Majority Voting Method (MVM); Model Sampling Method (MSM); and Centralized Training Method (CTM). Preliminary experimental tests were conducted with two synthetic data sets (eleven multiplexer and monks3) and a real world data set (intensive care medicine). The initial results demonstrate that the performance of DDM methods is competitive when compared with the CDM methods.; Fundação para a Ciência e a Tecnologia (FCT)

A Multi-Core Parallelization Strategy for Statistical Significance Testing in Learning Classifier Systems

Rudd, James; Moore, Jason H.; Urbanowicz, Ryan J.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /11/2013 Português
Relevância na Pesquisa
55.93%
Permutation-based statistics for evaluating the significance of class prediction, predictive attributes, and patterns of association have only appeared within the learning classifier system (LCS) literature since 2012. While still not widely utilized by the LCS research community, formal evaluations of test statistic confidence are imperative to large and complex real world applications such as genetic epidemiology where it is standard practice to quantify the likelihood that a seemingly meaningful statistic could have been obtained purely by chance. LCS algorithms are relatively computationally expensive on their own. The compounding requirements for generating permutation-based statistics may be a limiting factor for some researchers interested in applying LCS algorithms to real world problems. Technology has made LCS parallelization strategies more accessible and thus more popular in recent years. In the present study we examine the benefits of externally parallelizing a series of independent LCS runs such that permutation testing with cross validation becomes more feasible to complete on a single multi-core workstation. We test our python implementation of this strategy in the context of a simulated complex genetic epidemiological data mining problem. Our evaluations indicate that as long as the number of concurrent processes does not exceed the number of CPU cores...

An Analysis Pipeline with Statistical and Visualization-Guided Knowledge Discovery for Michigan-Style Learning Classifier Systems

Urbanowicz, Ryan J.; Granizo-Mackenzie, Ambrose; Moore, Jason H.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /11/2012 Português
Relevância na Pesquisa
65.99%
Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting and manual rule inspection. While this approach may be sufficient for simpler problems, the confounding influence of noise and the need to discriminate between predictive and non-predictive attributes calls for additional strategies. Additionally, tests of significance must be adapted to M-LCS analyses in order to make them a viable option within fields that require such analyses to assess confidence. In this work we introduce an M-LCS analysis pipeline that combines uniquely applied visualizations with objective statistical evaluation for the identification of predictive attributes, and reliable rule generalizations in noisy single-step data mining problems. This work considers an alternative paradigm for knowledge discovery in M-LCSs, shifting the focus from individual rules to a global, population-wide perspective. We demonstrate the efficacy of this pipeline applied to the identification of epistasis (i.e....

Pruning population size in XCS for complex problems

Rakitsch, Barbara; Bernauer, Andreas; Bringmann, Oliver; Rosenstiel, Wolfgang
Fonte: Universidade de Tubinga Publicador: Universidade de Tubinga
Tipo: Report (Bericht)
Português
Relevância na Pesquisa
55.97%
In this report, we show how to prune the population size of the Learning Classifier System XCS for complex problems. We say a problem is complex, when the number of specified bits of the optimal start classifiers (the prob lem dimension) is not constant. First, we derive how to estimate an equiv- alent problem dimension for complex problems based on the optimal start classifiers. With the equivalent problem dimension, we calculate the optimal maximum population size just like for regular problems, which has already been done. We empirically validate our results. Furthermore, we introduce a subsumption method to reduce the number of classifiers. In contrast to existing methods, we subsume the classifiers after the learning process, so subsuming does not hinder the evolution of optimal classifiers, which has been reported previously. After subsumption, the number of classifiers drops to about the order of magnitude of the optimal classifiers while the correctness rate nearly stays constant.

Inference in classifier systems

Muruzábal, Jorge
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /09/1993 Português
Relevância na Pesquisa
66.09%
Classifier systems (Css) provide a rich framework for learning and induction, and they have beenı successfully applied in the artificial intelligence literature for some time. In this paper, both theı architecture and the inferential mechanisms in general CSs are reviewed, and a number of limitations and extensions of the basic approach are summarized. A system based on the CS approach that is capable of quantitative data analysis is outlined and some of its peculiarities discussed.

PASS: a simple classifier system for data analysis

Muruzábal, Jorge
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /09/1993 Português
Relevância na Pesquisa
56.13%
Let x be a vector of predictors and y a scalar response associated with it. Consider the regression problem of inferring the relantionship between predictors and response on the basis of a sample of observed pairs (x,y). This is a familiar problem for which a variety of methods are available. This paper describes a new method based on the classifier system approach to problem solving. Classifier systems provide a rich framework for learning and induction, and they have been suc:cessfully applied in the artificial intelligence literature for some time. The present method emiches the simplest classifier system architecture with some new heuristic and explores its potential in a purely inferential context. A prototype called PASS (Predictive Adaptative Sequential System) has been built to test these ideas empirically. Preliminary Monte Carlo experiments indicate that PASS is able to discover the structure imposed on the data in a wide array of cases.

RTCS: a reactive with tags classifier system

Sanchis, Araceli; Molina, José M.; Isasi, Pedro; Segovia, Javier
Fonte: Springer Publicador: Springer
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em /04/2000 Português
Relevância na Pesquisa
56.1%
In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.

An enhanced classifier system for autonomous robot navigation in dynamic environments

Molina, José M.; Sanchis, Araceli; Berlanga, Antonio; Isasi, Pedro
Fonte: TSI Press, San Antonio, Texas, USA. Publicador: TSI Press, San Antonio, Texas, USA.
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2000 Português
Relevância na Pesquisa
56.2%
In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.

Evolution of tags in classifier systems

Sanchis, Araceli; Molina, José M.; Isasi, Pedro; Segovia, Javier
Fonte: Freund Publishing House Ltd Publicador: Freund Publishing House Ltd
Tipo: Artigo de Revista Científica Formato: application/pdf; text/plain
Publicado em //2001 Português
Relevância na Pesquisa
76.23%
One of the major problems related to Classifier Systems is the loss of rules. This loss is caused by the Genetic Algorithm being applied on the entire population of rules jointly. Obviously, the genetic operators discriminate rules by the strength value, such that evolution favors the generation of the stronger rules. When the learning process presents individual cases and allows the system to learn gradually from these cases, each learning interval with a set of individual cases can lead the strength to be distributed in favor of a given type of rules that would, in turn, be favored by the Genetic Algorithm. Basically, the idea is to divide rules into groups such that they are forced to remain in the system. This contribution is a method of learning that allows similar knowledge to be grouped. A field in which knowledge-based systems researchers have done a lot of work is concept classification and the relationships that are established between these concepts in the stage of knowledge conceptualization for later formalization. This job of classifying and searching relationships is performed in the proposed Classifier System by means of a mechanism, Tags, that allows the classification and the relationships to be discovered without the need for expert knowledge.

Probabilistic and fuzzy reasoning in simple learning classifier systems

Muruzábal, Jorge
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /04/1995 Português
Relevância na Pesquisa
65.92%
This paper is concerned with the general stimulus-response problem as addressed by a variety of simple learning c1assifier systems (CSs). We suggest a theoretical model from which the assessment of uncertainty emerges as primary concern. A number of representation schemes borrowing from fuzzy logic theory are reviewed, and sorne connections with a well-known neural architecture revisited. In pursuit of the uncertainty measuring goal, usage of explicit probability distributions in the action part of c1assifiers is advocated. Sorne ideas supporting the design of a hybrid system incorpo'rating bayesian learning on top of the CS basic algorithm are sketched.

Sobre cognição, adaptação e homeostase : uma analise de ferramentas computacionais bioinspiradas aplicadas a navegação autonoma de robos; On cognition, adaptation and homeostasis : analysis and synthesis of bio-inspired computational tools applied to robot autonomous navigation

Renan Cipriano Moioli
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 10/09/2008 Português
Relevância na Pesquisa
56.03%
Este trabalho tem como objetivos principais estudar, desenvolver e aplicar duas ferramentas computacionais bio-inspiradas em navegação autônoma de robôs. A primeira delas é representada pelos Sistemas Classificadores com Aprendizado, sendo que utilizou-se uma versão da proposta original, baseada em energia, e uma versão baseada em precisão. Adicionalmente, apresenta-se uma análise do processo de evolução das regras de inferência e da população final obtida. A segunda ferramenta trata de um modelo denominado sistema homeostático artificial evolutivo, composto por duas redes neurais artificiais recorrentes do tipo NSGasNets e um sistema endócrino artificial. O ajuste dos parâmetros do sistema é feito por meio de evolução, reduzindo-se a necessidade de codificação e parametrização a priori. São feitas análises de suas peculiaridades e de sua capacidade de adaptação. A motivação das duas propostas está no emprego conjunto de evolução e aprendizado, etapas consideradas fundamentais para a síntese de sistemas complexos adaptativos e modelagem computacional de processos cognitivos. Os experimentos visando validar as propostas envolvem simulação computacional em ambientes virtuais e implementações em um robô real do tipo Khepera II; The objectives of this work are to study...

Learning classifier systems with memory condition to solve non-Markov problems

Zang, Zhaoxiang; Li, Dehua; Wang, Junying
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/11/2012 Português
Relevância na Pesquisa
66.16%
In the family of Learning Classifier Systems, the classifier system XCS has been successfully used for many applications. However, the standard XCS has no memory mechanism and can only learn optimal policy in Markov environments, where the optimal action is determined solely by the state of current sensory input. In practice, most environments are partially observable environments on agent's sensation, which are also known as non-Markov environments. Within these environments, XCS either fails, or only develops a suboptimal policy, since it has no memory. In this work, we develop a new classifier system based on XCS to tackle this problem. It adds an internal message list to XCS as the memory list to record input sensation history, and extends a small number of classifiers with memory conditions. The classifier's memory condition, as a foothold to disambiguate non-Markov states, is used to sense a specified element in the memory list. Besides, a detection method is employed to recognize non-Markov states in environments, to avoid these states controlling over classifiers' memory conditions. Furthermore, four sets of different complex maze environments have been tested by the proposed method. Experimental results show that our system is one of the best techniques to solve partially observable environments...

A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

Howard, David; Bull, Larry; Lanzi, Pier-Luca
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 31/08/2015 Português
Relevância na Pesquisa
56.17%
Learning Classifier Systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a Genetic Algorithm (GA) to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding "macro-actions," created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task.

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

Preen, Richard J.; Bull, Larry
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
66.15%
A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.

Knowledge Representation in Learning Classifier Systems: A Review

Shoeleh, Farzaneh; Majd, Mahshid; Hamzeh, Ali; Hashemi, Sattar
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/06/2015 Português
Relevância na Pesquisa
76.15%
Knowledge representation is a key component to the success of all rule based systems including learning classifier systems (LCSs). This component brings insight into how to partition the problem space what in turn seeks prominent role in generalization capacity of the system as a whole. Recently, knowledge representation component has received great deal of attention within data mining communities due to its impacts on rule based systems in terms of efficiency and efficacy. The current work is an attempt to find a comprehensive and yet elaborate view into the existing knowledge representation techniques in LCS domain in general and XCS in specific. To achieve the objectives, knowledge representation techniques are grouped into different categories based on the classification approach in which they are incorporated. In each category, the underlying rule representation schema and the format of classifier condition to support the corresponding representation are presented. Furthermore, a precise explanation on the way that each technique partitions the problem space along with the extensive experimental results is provided. To have an elaborated view on the functionality of each technique, a comparative analysis of existing techniques on some conventional problems is provided. We expect this survey to be of interest to the LCS researchers and practitioners since it provides a guideline for choosing a proper knowledge representation technique for a given problem and also opens up new streams of research on this topic.

A Spiking Neural Learning Classifier System

Howard, Gerard; Bull, Larry; Lanzi, Pier-Luca
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 16/01/2012 Português
Relevância na Pesquisa
66%
Learning Classifier Systems (LCS) are population-based reinforcement learners used in a wide variety of applications. This paper presents a LCS where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. We employ a constructivist model of growth of both neurons and dendrites that realise flexible learning by evolving structures of sufficient complexity to solve a well-known problem involving continuous, real-valued inputs. Additionally, we extend the system to enable temporal state decomposition. By allowing our LCS to chain together sequences of heterogeneous actions into macro-actions, it is shown to perform optimally in a problem where traditional methods can fail to find a solution in a reasonable amount of time. Our final system is tested on a simulated robotics platform.; Comment: 20 pages

Projective simulation for classical learning agents: a comprehensive investigation

Mautner, Julian; Makmal, Adi; Manzano, Daniel; Tiersch, Markus; Briegel, Hans J.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
55.96%
We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced [H.J. Briegel and G. De las Cuevas. Sci. Rep. 2, 400, (2012)]. Here we provide a detailed analysis of the model and examine its performance, including its achievable efficiency, its learning times and the way both properties scale with the problems' dimension. In addition, we situate the PS agent in different learning scenarios, and study its learning abilities. A variety of new scenarios are being considered, thereby demonstrating the model's flexibility. Furthermore, to put the PS scheme in context, we compare its performance with those of Q-learning and learning classifier systems, two popular models in the field of reinforcement learning. It is shown that PS is a competitive artificial intelligence model of unique properties and strengths.; Comment: Accepted for publication in New Generation Computing. 23 pages, 23 figures

Exploiting generalisation symmetries in accuracy-based learning classifier systems: An initial study

Bull, Larry
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 10/01/2014 Português
Relevância na Pesquisa
76.05%
Modern learning classifier systems typically exploit a niched genetic algorithm to facilitate rule discovery. When used for reinforcement learning, such rules represent generalisations over the state-action-reward space. Whilst encouraging maximal generality, the niching can potentially hinder the formation of generalisations in the state space which are symmetrical, or very similar, over different actions. This paper introduces the use of rules which contain multiple actions, maintaining accuracy and reward metrics for each action. It is shown that problem symmetries can be exploited, improving performance, whilst not degrading performance when symmetries are reduced.; Comment: 6 pages, 13 figures

A Brief History of Learning Classifier Systems: From CS-1 to XCS

Bull, Larry
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
76.08%
Modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an historical overview of the evolution of such systems up to XCS, and then some of the subsequent developments of XCS to different types of learning.; Comment: 37 pages, 9 figures