Feature selection from microarray data : Genetic algorithm based approach. Issue 8 (17th November 2019)

• Record Type: Journal Article
• Authors: Ram, Pintu Kumar; Kuila, Pratyay
• Is Part Of: Journal of information & optimization sciences. Volume 40:Issue 8(2019)
• Page Start: 1599
• Page End: 1610
• Publication Date: 2019-11-17
• Subjects: Computer Science; Feature Selection -- T-score -- F-score -- GA -- Microarray Technology; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2019.1703260
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: The use of microarray data for the feature (gene) selection is continuously increasing in the field of health care to diagnose the disease. Now it becomes a trend to find the subset of feature by using traditional algorithms. Most of the researches have used intelligent algorithm for the same to predict the diseases and take necessary action as per the requirement. In addition, a minimum feature set can be useful to prognosis the disease in contrast to a huge feature set. Inspired by this, we built a model based on genetic algorithm to select the minimum feature set with high accuracy from large microarray data. We have applied the machine learning classifier to get the accuracy of the features. For experimental analysis, we use the cancer based microarray gene expressed data and compare the simulation result with Differential Evolution.