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2015 | 24 |
Article title

Feature Selection and Classification Pairwise Combinations for High-dimensional Tumour Biomedical Datasets

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PL
Abstracts
PL
This paper concerns classification of high-dimensional yet small sample size biomedical data and feature selection aimed at reducing dimensionality of the microarray data. The research presents a comparison of pairwise combinations of six classification strategies, including decision trees, logistic model trees, Bayes network, Na¨ıve Bayes, k-nearest neighbours and sequential minimal optimization algorithm for training support vector machines, as well as seven attribute selection methods: Correlation-based Feature Selection, chi-squared, information gain, gain ratio, symmetrical uncertainty, ReliefF and SVM-RFE (Support Vector Machine-Recursive Feature Elimination). In this paper, SVMRFE feature selection technique combined with SMO classifier has demonstrated its potential ability to accurately and efficiently classify both binary and multiclass high-dimensional sets of tumour specimens.
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Year
Volume
24
Physical description
Dates
published
2015
online
06 - 07 - 2016
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Publication order reference
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YADDA identifier
bwmeta1.element.ojs-issn-2083-8476-year-2015-volume-24-article-6334
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