Feature Selection in Data-Mining for Genetics Using Genetic Algorithm

Abstract

Abstract:We discovered genetic features and environmental factors which were involved in multifactorial diseases. To exploit the massive data obtained from the experiments conducted at the General Hospital, Chennai, data mining tools were required and we proposed a 2-Phase approach using a specific genetic algorithm. This heuristic approach had been chosen as the number of features to consider was large (upto 3654 for biological data under our study). Collected data indicated for pairs of affected individuals of a same family their similarity at given points (locus) of their chromosomes. This was represented in a matrix where each locus was represented by a column and each pairs of individuals considered by a row. The objective was first to isolate the most relevant associations of features and then to class individuals that had the considered disease according to these associations. For the first phase, the feature selection problem, we used a genetic algorithm (GA). To deal with this very specific problem, some advanced mechanisms had been introduced in the genetic algorithm such as sharing, random immigrant, dedicated genetic operators and a particular distance operator had been defined. Then, the second phase, a clustering based on the features selected during the previous phase, will use the clustering algorithm k-means.

An analysis of modified naïve Bayesian classification using correlation based clustering for gene sequence data analysis

“…This shows that the k-means algorithm using results of the GA, is able to construct clusters very closely related to the solution pre-sented in results of the workshop. Moreover this solution has been exactly found 4 times over 10 of executions [3].The genetic algorithm managed to select interesting features and the k-means algorithm was able to class pairs of individuals according to these features and to confirm interesting associations of features [3].  Chan Wai Keung Brian (2006): The advantage of using genetic algorithm [4] is that it does not have to know any rules of the prob-lem in advancethe rule will can be found through evolution.…”

Correlation Based Clustering and the Modified Naïve Bayesian Classification for Gene Sequence Data Analysis

“…This shows that the k-means algorithm using results of the GA, is able to construct clusters very closely related to the solution presented in results of the workshop. Moreover this solution has been exactly found 4 times over 10 of executions [3].…”

“…The genetic algorithm managed to select interesting features and the k-means algorithm was able to class pairs of individuals according to these features and to confirm interesting associations of features [3].…”

Feature Subset Selection for Hot Method Prediction using Genetic Algorithm wrapped with Support Vector Machines

“…Several researchers ( Vafaie and Jong, 1992;Kohavi and John, 1997;Yang and Honavar, 1998;Pernkopf and O'Leary, 2001;Fröhlich and Chapelle, 2003;Yu and Cho, 2006;Huang and Wang, 2006;Faraoun and Rabhi, 2007;Rajavarman et al, 2007;Ramirez and Puiggros, 2007;Xia et al, 2009) have employed the genetic algorithm as a search tool in feature subset selection in their work. All these investigations have confirmed that the GA-generated feature subsets perform better than the initial universal set or the full feature set.…”