Performance Analysis of Microarray Data Classification using Machine Learning Techniques

Performance Analysis of Microarray Data Classification using Machine Learning Techniques

Subhendu Kumar Pani (Biju Patnaik University of Technology, Bhubaneswar, India), Bikram Kesari Ratha (Utkal University, Bhubaneswar, India) and Ajay Kumar Mishra (PN Autonomous College, Khordha, Bhubaneswar, India)
Copyright: © 2015 |Pages: 12
DOI: 10.4018/IJKDB.2015070104
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

Microarray technology of DNA permits simultaneous monitoring and determining of thousands of gene expression activation levels in a single experiment. Data mining technique such as classification is extensively used on microarray data for medical diagnosis and gene analysis. However, high dimensionality of the data affects the performance of classification and prediction. Consequently, a key issue in microarray data is feature selection and dimensionality reduction in order to achieve better classification and predictive accuracy. There are several machine learning approaches available for feature selection. In this study, the authors use Particle Swarm Organization (PSO) and Genetic Algorithm (GA) to find the performance of several popular classifiers on a set of microarray datasets. Experimental results conclude that feature selection affects the performance.
Article Preview

2. Feature Selection

In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the method of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection methods are used for three reasons:

  • Simplification of models to formulate them easier to interpret by researchers/users;

  • Shorter training times;

  • Enhanced generalization by decreasing overfitting (formally, reduction of variance).

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 7: 2 Issues (2017)
Volume 6: 2 Issues (2016)
Volume 5: 2 Issues (2015)
Volume 4: 2 Issues (2014)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing