Volume 14, number 1
 PDF Downloads: 1325

A New Feature Selection Techniques Using Genetics Search and Random Search Approaches for Breast Cancer

Tamilvanan1 and V. Murali Bhaskaran2

1Research and Development Centre, Bharathiar University, Coimbatore-641046, Tamil Nadu, India.

2Dhirajlal Gandhi College of Technology, Salem-636290, Tamil Nadu, India.

DOI : http://dx.doi.org/10.13005/bbra/2459

ABSTRACT: In this paper mainly deals with various classification algorithm techniques with feature extraction algorithm used to improve the predicated accuracy of the algorithm. This paper applied with correlation based feature selection as a feature evaluator and Genetics and random searching method. The results of the classification model are sensitive, specificity, precision, time, and accuracy. Finally, it concludes that the proposed CFL-NB algorithm performance is better than other classification algorithms techniques for breast cancer disease.

KEYWORDS: Correlation-based Feature Selection; Data Mining; Genetic Algorithm; Random Search and Naive Bayes Algorithm

Download this article as: 
Copy the following to cite this article:

Tamilvanan T, Bhaskaran V. M. A New Feature Selection Techniques Using Genetics Search and Random Search Approaches for Breast Cancer. Biosci Biotechnol Res Asia 2017;14(1)
Copy the following to cite this URL:

Tamilvanan T, Bhaskaran V. M. A New Feature Selection Techniques Using Genetics Search and Random Search Approaches for Breast Cancer. Biosci Biotechnol Res Asia 2017;14(1) Available from: https://www.biotech-asia.org/?p=21840

[ HTML Full Text]

Back to TOC