Title: Feature subset selection for cancer detection using various rank-based algorithms

Authors: B. Surendiran; P. Sreekanth; E. Sri Hari Keerthi; M. Praneetha; D. Swetha; N. Arulmurugaselvi

Addresses: Department of CSE, National Institute of Technology Puducherry, Karaikal, India ' Department of CSE, Madanapalle Institute of Technology and Science, Madanapalle, India ' Department of CSE, Madanapalle Institute of Technology and Science, Madanapalle, India ' Department of CSE, Madanapalle Institute of Technology and Science, Madanapalle, India ' Department of CSE, Madanapalle Institute of Technology and Science, Madanapalle, India ' GPT Coimbatore, Tamilnadu, India

Abstract: Feature selection in data mining is the process of identifying the profitable features that are more significant in giving accurate results. Feature selection approaches like filter method and wrapper method are used here to get the more significant attributes. These methods generate the list of highly important attributes by using various ranker algorithms like correlation, relief-F, information gain, Gini index and classifiers like OneR, support vector machine, naive Bayes, random tree. In this, we are using ranker methods to perform feature selection on breast cancer analysis. Various experiments have been carried out on breast cancer Coimbra dataset using different classifiers to predict the accuracy. The crucial attributes are identified using feature selection methods, analysed for both balanced and unbalanced datasets and classified using OneR classifier.

Keywords: feature selection; filter and wrapper methods; breast cancer; ranker algorithm; balanced dataset.

DOI: 10.1504/IJMEI.2021.115969

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.4, pp.346 - 357

Received: 27 Mar 2019
Accepted: 14 Dec 2019
Published online: 06 Jul 2021 *

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