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A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection

A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection

P. K. Nizar Banu, H. Hannah Inbarani
Copyright: © 2013 |Volume: 2 |Issue: 4 |Pages: 14
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781466635692|DOI: 10.4018/ijsda.2013100103
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MLA

Banu, P. K. Nizar, and H. Hannah Inbarani. "A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection." IJSDA vol.2, no.4 2013: pp.33-46. http://doi.org/10.4018/ijsda.2013100103

APA

Banu, P. K. & Inbarani, H. H. (2013). A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection. International Journal of System Dynamics Applications (IJSDA), 2(4), 33-46. http://doi.org/10.4018/ijsda.2013100103

Chicago

Banu, P. K. Nizar, and H. Hannah Inbarani. "A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection," International Journal of System Dynamics Applications (IJSDA) 2, no.4: 33-46. http://doi.org/10.4018/ijsda.2013100103

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Abstract

As the micro array databases increases in dimension and results in complexity, identifying the most informative genes is a challenging task. Such difficulty is often related to the huge number of genes with very few samples. Research in medical data mining addresses this problem by applying techniques from data mining and machine learning to the micro array datasets. In this paper Unsupervised Tolerance Rough Set based Quick Reduct (U-TRS-QR), a diverse feature selection algorithm, which extends the existing equivalent rough sets for unsupervised learning, is proposed. Genes selected by the proposed method leads to a considerably improved class predictions in wide experiments on two gene expression datasets: Brain Tumor and Colon Cancer. The results indicate consistent improvement among 12 classifiers.

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