DHS:An unsupervised feature selection algorithm based on Harmony Search for Microarray data Classification

Authors

  • K.Umamaheswar
  • M.Dhivya

Abstract

Feature Selection is an essential task in microarray data classification.Various methods are available to handle the data with class labels whereas some data are mislabeled and unreliable. Unsupervised gene selection methods are existing to handle such data. We propose an unsupervised filter based method known as dynamic Harmony Search(DHS) which integrates Harmony Search into filter approach by defining new fitness function and it is independent of any learning model.The main aim of the filter approach is to quantify the relevance based on the intrinsic properties of the data.The proposed method is applied on benchmark microarray datasets and the results are compared with well known unsupervised gene selection methods using different classifiers. The proposed method governs promising enhancement on feature selection and good classification accuracy.

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Published

2016-10-01

How to Cite

K.Umamaheswar, & M.Dhivya. (2016). DHS:An unsupervised feature selection algorithm based on Harmony Search for Microarray data Classification. Journal of Network and Innovative Computing, 4, 10. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/125

Issue

Vol. 4 (2016)

Section

Original Article

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