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

Publication date: 11.04.2016

Schedae Informaticae, 2015, Volume 24, pp. 53 - 62

https://doi.org/10.4467/20838476SI.15.005.3027

Authors

,
Agnieszka Wosiak
Institute of Information Technology Lodz University of Technology
All publications →
Agata Dziomdziora
Institute of Information Technology Lodz University of Technology
All publications →

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Titles

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

Abstract

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.

Keywords

References

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Information

Information: Schedae Informaticae, 2015, Volume 24, pp. 53 - 62

Article type: Original article

Titles:

Polish:

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

English:

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

Authors

Institute of Information Technology Lodz University of Technology

Institute of Information Technology Lodz University of Technology

Published at: 11.04.2016

Article status: Open

Licence: None

Percentage share of authors:

Agnieszka Wosiak (Author) - 50%
Agata Dziomdziora (Author) - 50%

Article corrections:

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Publication languages:

English

View count: 2013

Number of downloads: 2170

<p> Feature Selection and Classification Pairwise Combinations for High-dimensional Tumour Biomedical Datasets</p>
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