ProgramsView all programs
From the news
- Chancellor Amma Addresses the Parliament of World’s Religions
- Amrita Students Qualify for the European Mars Rover Challenge
Publication Type : Journal Article
Publisher : Lecture Notes in Computational Vision and Biomechanics
Source : Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 28, p.1-10 (2018)
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2018
Abstract : Attribute selection is an important step in the analysis of gene expression for cancer or illnesses in general. The huge dimensionality of gene expression data that includes many insignificant and redundant genes reduces the classification accuracy. In this study, we propose a hybrid attribute selection method to identify the small set of the most significant genes associated with the cause of cancer. The proposed method integrates the advantages of filter and a wrapper to perform attribute selection by devising a customized fitness function for the genetic algorithm. Three data sets are used that includes leukemia, CNS and colon cancer. Results of our technique are compared with the other standard techniques available in literature. The proposed hybrid approach produces comparably better accuracy than the standard implementation of the genetic algorithm.
Cite this Research Publication : A. Chinnaswamy, Ramakrishnan, S., and Dheeraj, S. S., “Genetic Algorithm Based Hybrid Attribute Selection using Customized Fitness Function”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 1-10, 2018.