Evolutionary sequential genetic search technique-based cancer classification using fuzzy rough nearest neighbour classifier.
Healthc Technol Lett
; 5(4): 130-135, 2018 Aug.
Article
in En
| MEDLINE
| ID: mdl-30155265
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 compared to other feature selection algorithms.
FRNN classifier; GSFR feature selection algorithm; boundary regions; cancer; classification accuracy; computation time; deadly diseases; evolutionary sequential genetic search technique-based cancer classification; feature selection; fitness function; fuzzy rough nearest neighbour classifier; fuzzy rough set; fuzzy set theory; generated subset; genetic algorithms; genetic operator; genetic search fuzzy rough feature selection algorithm; mathematical operators; medical computing; modified dependency function; pattern classification; positive regions; rough set theory; search problems
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Healthc Technol Lett
Year:
2018
Document type:
Article
Affiliation country:
India