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Evolutionary optimization of radial basis function classifiers for data mining applications (2005)
| Venue: | IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics |
| Citations: | 16 - 0 self |
BibTeX
@ARTICLE{Buchtala05evolutionaryoptimization,
author = {Oliver Buchtala and Manuel Klimek and Bernhard Sick},
title = {Evolutionary optimization of radial basis function classifiers for data mining applications},
journal = {IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics},
year = {2005},
volume = {35},
pages = {928--947}
}
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Abstract
Abstract—In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms. Index Terms—Data mining, evolutionary algorithm (EA), feature selection, model selection, radial basis function (RBF) network. I.
Keyphrases
evolutionary optimization radial basis function classifier model selection data mining application evolutionary algorithm radial basis function direct marketing method biometric signature verification structure parameter key task optimization effort data mining problem lazy evaluation intrusion detection classification problem computer network chemical production specific application many idea penalty term temperature-based adaptive control various technique rbf network index term data mining customer acquisition soft constraint appropriate input feature hybrid training data set feature selection possible feature ea-based rbf optimization technique error rate many data mining application
