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Erschienen in:
Complex and Composite Graphical Symbol Recognition and Retrieval: A Quick Review Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Combination of Feature Selection Methods for the Effective Classification of Microarray Gene Expression Data

verfasst von : T. Sheela, Lalitha Rangarajan

Erschienen in: Recent Trends in Image Processing and Pattern Recognition

Verlag: Springer Singapore

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Abstract

Gene selection from microarray gene expression data is very difficult due to the large dimensionality of the data. The number of samples in the microarray data set is very small compared to the number of genes as features. To reduce dimensionality, selection of significant genes is necessary. An effective method of gene feature selection helps in dimensionality reduction and improves the performance of the sample classification. In this work, we have examined if combination of feature selection methods can improve the performance of classification algorithms. We propose two methods of combination of feature selection techniques. Experimental results suggest that appropriate combination of filter gene selection methods is more effective than individual techniques for microarray data classification. We have compared our combination methods using different learning algorithms.

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Vorheriges Kapitel Document Image Analysis for a Major Indic Script Bangla - Advancement and Scope
Nächstes Kapitel Pattern Recognition Based on Hierarchical Description of Decision Rules Using Choquet Integral
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Metadaten
Titel
Combination of Feature Selection Methods for the Effective Classification of Microarray Gene Expression Data
verfasst von
T. Sheela
Lalitha Rangarajan
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Recent Trends in Image Processing and Pattern Recognition

Print ISBN: 978-981-10-4858-6

Electronic ISBN: 978-981-10-4859-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-4859-3_13