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A Hybrid Filter-Wrapper Approach for FeatureSelection
Örebro University, School of Science and Technology, Örebro University, Sweden.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Feature selection is the task of selecting a small subset from original features

that can achieve maximum classification accuracy. This subset of features has

some very important benefits like, it reduces computational complexity of learning

algorithms, saves time, improve accuracy and the selected features can be

insightful for the people involved in problem domain. This makes feature selection

as an indispensable task in classification task.

This dissertation presents a two phase approach for feature selection. In the

first phase a filter method is used with “correlation coefficient” and “mutual

information” as statistical measure of similarity. This phase helps in improving

the classification performance by removing redundant and unimportant

features. A wrapper method is used in the second phase with the sequential

forward selection and sequential backward elimination. This phase helps in selecting

relevant feature subset that produce maximum accuracy according to

the underlying classifier. The Support Vector Machine (SVM) classifier (linear

and nonlinear) is used to evaluate the classification accuracy of our approach.

This empirical results of commonly used data sets from the University of

California, Irvine repository and microarray data sets showed that the proposed

method performs better in terms of classification accuracy, number of selected

features, and computational efficiency.


Place, publisher, year, edition, pages
2011. , 104 p.
National Category
Computer Engineering
URN: urn:nbn:se:oru:diva-26418ISRN: ORU-NAT/DAT-AS-2012/0010--SEOAI: diva2:567115
Subject / course
Computer Engineering
Available from: 2012-11-09 Created: 2012-11-12 Last updated: 2012-11-12Bibliographically approved

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School of Science and Technology, Örebro University, Sweden
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