(Knowledge Extraction based onEvolutionary Learning) tool, an open source software that supports datamanagement and a designer of experiments. KEEL pays special attentionto the implementation of evolutionary learning and soft computing basedtechniques for Data Mining problems including regression, classiﬁcation,clustering, pattern mining and so on.The aim of this paper is to present three new aspects of KEEL: KEEL-dataset, a data set repository which includes the data set partitions in theKEELformatandshowssomeresultsofalgorithmsinthesedatasets; someguidelines for including new algorithms in KEEL, helping the researcherstomaketheirmethodseasilyaccessibletootherauthorsandtocomparetheresults of many approaches already included within the KEEL software;and a module of statistical procedures developed in order to provide to theresearcher a suitable tool to contrast the results obtained in any experimen-talstudy.Acaseofstudyisgiventoillustrateacompletecaseofapplicationwithin this experimental analysis framework.

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KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework

This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.

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KEEL: a software tool to assess evolutionary algorithms for data mining problems

Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.

Introduction to Fuzzy Logic using MATLAB

An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.

A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

Ten years of genetic fuzzy systems: current framework and new trends

Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach Genetic Fuzzy Rule-Based Systems Based on the lterative Rule Learning Approach Other Genetic Fuzzy Rule-Based System Other Kinds of Evolutionary Fuzzy Systems Applications.

Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases

Learning with Genetic Algorithms

Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach

Decision trees are suitable for classiication problems in which instances are represented by attribute-value pairs and the target concept has discrete values. A decision tree classiies an example by propagating it along a path from the root node down to a leaf node which contains the classiication for this example. Each node tests a particular attribute, each of its branches corresponds to a particular value of this attribute. Figure 9.1 shows a typical decision tree that predicts if a customer qualiies for credit based on its annual income and employment status. Each instance is classiied according to the attributes income=flow, medium,highg and employment=fnone, part time, fullg. The example employment=full, income=high, is sorted down the two right branches and categorized as a positive example according to the leaf node. Notice, that a path to a leaf node, does not necessarily use very attribute to obtain a classiication. All customers with no employment are classiied as negative examples, regardless of their income.

Luis Magdalena

Papers

Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases

Learning with Genetic Algorithms

Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach

Other Kinds of Evolutionary Fuzzy Systems