Call for Papers

Apr 2019 - Volume 10, Issue 2
Deadline: 5 Apr 2019
Notification: 15 Apr 2019
Publication: 30 Apr 2019

June 2019 - Volume 10, Issue 3
Deadline: 5 June 2019
Notification: 15 June 2019
Publication: 30 June 2019

Indexed in


Title : Hybrid Local Feature Selection In DNA Analysis Based Cancer Classification
Authors : Mrs.M.Akila, Mr.S.Senthamarai kannan
Keywords : Feature Selection, Memetic Algorithm, Filters ,wrappers, genetic algorithm, symmetrical uncertainty.
Issue Date : Jun-Jul 2012
Abstract :
Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data and increasing learning accuracy. The development of microarray dataset technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it helps us to exactly predict and diagnose cancer. To precisely classify cancer we have to select genes related to cancer. The challenging task in cancer diagnosis is how to identify salient expression genes from thousands of genes in microarray data because extracted genes from microarray dataset have many unwanted datas not related to cancer. In this project we attempt explore a novelhybrid wrapper and filter feature selection algorithm for classification problem using a memetic framework i.e., a combination of genetic algorithm (GA) and local search (LS) has been proposed.. The LS is performed using correlation based filter methods are discritize, ranking and redundancy elimination with symmetrical uncertainty (SU) measure .using this hybrid method we can able find cancer related gene, From the larger amount of gene datas .using that smaller dataset doctors can able to find the affected gene and provide better treatment. The efficiency and the effectiveness of the method are demonstrated through extensive comparisons with other methods using real-world datasets of high dimentionality
Page(s) : 470-475
ISSN : 0976-5166
Source : Vol. 3, No.3