Article: Optimal feature selection for classification using rough set-based CGA-NN classifier Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2016 Vol.11 No.4 pp.357 - 378 Abstract: The overall process of the classification method is divided into two main steps, such as: 1) feature selection using RS-MKFCM algorithm; 2) classification using CGA-based neural network classifier. At first, the multiple kernel fuzzy c-means clustering with rough set theory (RS-MKFCM) algorithm is applied on the high dimensional micro array dataset to select the important features. After that, the classification is done through CGA-NN classifier. Selected features from micro array dataset are collected and fed to the neural network for training. In neural network, we utilise scaled conjugate gradient algorithm for training. It provides faster training with excellent test efficiency. To improve the classification performance, hybridisation of cuckoo search and genetic algorithm (CGA) is utilised with neural network for weight optimisation process. At last, the experimentation is performed by means of the five different micro array dataset. The experimentation result proves that the CGA-NN classifier outperformed the existing approach by attaining the maximum accuracy of 98.93% for ovarian cancer dataset when compared existing NN only achieved 51.87% and also support vector machine classifier achieves the 88.23%. Inderscience Publishers - linking academia, business and industry through research

Title: Optimal feature selection for classification using rough set-based CGA-NN classifier

Authors: S. Gilbert Nancy; S. Appavu

Addresses: Computer Science and Engineering, Ultra College of Engineering for Women, Madurai, India ' KLN College of Information Technology, Madurai, India

Abstract: The overall process of the classification method is divided into two main steps, such as: 1) feature selection using RS-MKFCM algorithm; 2) classification using CGA-based neural network classifier. At first, the multiple kernel fuzzy c-means clustering with rough set theory (RS-MKFCM) algorithm is applied on the high dimensional micro array dataset to select the important features. After that, the classification is done through CGA-NN classifier. Selected features from micro array dataset are collected and fed to the neural network for training. In neural network, we utilise scaled conjugate gradient algorithm for training. It provides faster training with excellent test efficiency. To improve the classification performance, hybridisation of cuckoo search and genetic algorithm (CGA) is utilised with neural network for weight optimisation process. At last, the experimentation is performed by means of the five different micro array dataset. The experimentation result proves that the CGA-NN classifier outperformed the existing approach by attaining the maximum accuracy of 98.93% for ovarian cancer dataset when compared existing NN only achieved 51.87% and also support vector machine classifier achieves the 88.23%.

Keywords: kernel FCM; fuzzy c-means clustering; rough set theory; neural networks; scaled conjugate gradient; SCG; cuckoo search; genetic algorithms; support vector machines; SVM; optimisation; feature selection; microarrays; ovarian cancer; classification; metaheuristics.

DOI: 10.1504/IJBIDM.2016.082212

International Journal of Business Intelligence and Data Mining, 2016 Vol.11 No.4, pp.357 - 378

Received: 28 Jul 2016
Accepted: 13 Sep 2016
Published online: 12 Feb 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Optimal feature selection for classification using rough set-based CGA-NN classifier

Keep up-to-date