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

Healthcare Technology Letters

article

Loganathan Meenachi¹  Srinivasan Ramakrishnan¹ 

[1] Department of Information Technology, Dr.Mahalingam College of Engineering and Technology

关键词: search problems;    pattern classification;    cancer;    fuzzy set theory;    rough set theory;    feature selection;    genetic algorithms;    medical computing;    mathematical operators;    evolutionary sequential genetic search technique-based cancer classification;    fuzzy rough nearest neighbour classifier;    deadly diseases;    genetic search fuzzy rough feature selection algorithm;    fuzzy rough set;    genetic operator;    generated subset;    modified dependency function;    GSFR feature selection algorithm;    FRNN classifier;    positive regions;    boundary regions;    fitness function;    classification accuracy;    computation time;   

DOI  :  10.1049/htl.2018.5041

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

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 compared to other feature selection algorithms.

【 授权许可】

CC BY|CC BY-ND|CC BY-NC|CC BY-NC-ND