doi: 10.7763/IJCTE.2009.V1.25
An Approach for Discretization and Feature Selection Of Continuous-Valued Attributes in Medical Images for Classification Learning
- 2Department of Master of Computer Applications of St. Joseph’s Engineering College, Chennai. India which is affiliated to the Anna University and accredited by the AICTE, New Delhi, India.
Abstract
Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization and, identify defining characteristics of the method. We then propose a new supervised approach which combines discretization and feature selection to select the most relevant features which can be used for classification purpose. The classification technique to be used is Associatve Classifiers. The features used are Harlick Texture features extracted from MRI Images. The results show that the proposed method is efficient and well-suited to perform preprocessing of continuous valued attributes.
Keywords
- Classifier
- Discretization
- Feature Selection
- MRI
How to Cite
Jaba Sheela L and V. Shanthi, "An Approach for Discretization and Feature Selection Of Continuous-Valued Attributes in Medical Images for Classification Learning," International Journal of Computer Theory and Engineering, vol. 1, no. 2, pp. 154-158, 2009. https://doi.org/10.7763/IJCTE.2009.V1.25
Copyright & License
Copyright © 2009 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).