Non-incremental classification learning algorithms based on voting feature intervals

buir.supervisorGüvenir, Halil Altay
dc.contributor.authorDemiröz, Gülşen
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionCataloged from PDF version of article. en_US
dc.description Thesis (Master's): Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1997.en_US
dc.descriptionIncludes bibliographical references (leaves 147-154).en_US
dc.description.abstractLearning is one of the necessary abilities of an intelligent agent. This thesis proposes several learning algorithms for multi-concept descriptions in the form of feature intervals, called Voting Feature Intervals (VFI) algorithms. These algorithms are non-incremental classification learning algorithms, and use feature projection based knowledge representation for the classification knowledge induced from a set of preclassified examples. The concept description learned is a set of intervals constructed separately for each feature. Each interval carries classification information for all classes. The classification of an unseen instance is based on a voting scheme, where each feature distributes its vote among all classes. Empirical evaluation of the VFI algorithms has shown that they are the best performing algorithms among other previously developed feature projection based methods in term of classification accuracy. In order to further improve the accuracy, genetic algorithms are developed to learn the optimum feature weights for any given classifier. Also a new crossover operator, called continuous uniform crossover, to be used in this weight learning genetic algorithm is proposed and developed during this thesis. Since the explanation ability of a learning system is as important as its accuracy, VFI classifiers are supplemented with a facility to convey what they have learned in a comprehensible way to humans.
dc.description.statementofresponsibility Gülşen Demiröz en_US
dc.format.extentxix, 161 leaves ; 30 cmen_US
dc.publisherBilkent Universityen_US
dc.subjectMachine learning
dc.subjectSupervised learning
dc.subjectInductive learning
dc.subjectNon-incremental learning
dc.subjectFeature intervals
dc.subjectGenetic algorithms
dc.titleNon-incremental classification learning algorithms based on voting feature intervalsen_US
dc.title.alternativeOylayan öznitelik bölüntülerine dayalı toplu sınıflandırma öğrenme algoritmaları
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