Evolutionary sequential genetic search technique-based cancer classification using fuzzy rough nearest neighbour classifier

Healthcare Technology Letters
Loganathan Meenachi, Srinivasan Ramakrishnan

Abstract

Cancer is one of the deadly diseases of human life. The patient may likely to survive if the disease is diagnosed in its early stages. In this Letter, the authors propose a genetic search fuzzy rough (GSFR) feature selection algorithm, which is hybridised using the evolutionary sequential genetic search technique and fuzzy rough set to select features. The genetic operator's selection, crossover and mutation are applied to generate the subset of features from dataset. The generated subset is subjected to the evaluation with the modified dependency function of the fuzzy rough set using positive and boundary regions, which act as a fitness function. The generation and evaluation of the subset of features continue until the best subset is arrived at to develop the classification model. Selected features are applied to the different classifiers, from the classifiers fuzzy-rough nearest neighbour (FRNN) classifier, which outperforms in terms of classification accuracy and computation time. Hence, the FRNN is applied for performance analysis of existing feature selection algorithms against the proposed GSFR feature selection algorithm. The result generated from the proposed GSFR feature selection algorithm proved to be precise when c...Continue Reading

References

Dec 3, 2013·Computers in Biology and Medicine·Vasileios Ch KorfiatisGeorge K Matsopoulos
Feb 25, 2014·Computers in Biology and Medicine·Wengang Zhou, Julie A Dickerson
Oct 14, 2016·Healthcare Technology Letters·Georgia Spiridon KaranasiouDimitrios Ioannis Fotiadis
May 27, 2017·Journal of Biomedical Informatics·Yang LiuYang Huang
Feb 13, 2018·Computational Biology and Chemistry·Luigi Leonardo Palese

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