Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
GMDH-based feature ranking and selection for improved classification of medical data
Journal of Biomedical Informatics
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
Computer Methods and Programs in Biomedicine
Rough Computing: Theories, Technologies and Applications
Rough Computing: Theories, Technologies and Applications
Feature subset selection in large dimensionality domains
Pattern Recognition
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis
Journal of Medical Systems
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Discretization of continuous attributes in rough set theory and its application
CIS'04 Proceedings of the First international conference on Computational and Information Science
A hybrid fish swarm optimisation algorithm for solving examination timetabling problems
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Computer Methods and Programs in Biomedicine
Hybrid algorithm based on particle swarm optimization and artificial fish swarm algorithm
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Wrapper feature selection for small sample size data driven by complete error estimates
Computer Methods and Programs in Biomedicine
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
Expert Systems with Applications: An International Journal
Feature selection for medical diagnosis: Evaluation for cardiovascular diseases
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Medical datasets are often classified by a large number of disease measurements and a relatively small number of patient records. All these measurements (features) are not important or irrelevant/noisy. These features may be especially harmful in the case of relatively small training sets, where this irrelevancy and redundancy is harder to evaluate. On the other hand, this extreme number of features carries the problem of memory usage in order to represent the dataset. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Thus, the learning model receives a concise structure without forfeiting the predictive accuracy built by using only the selected prominent features. Therefore, nowadays, FS is an essential part of knowledge discovery. In this study, new supervised feature selection methods based on hybridization of Particle Swarm Optimization (PSO), PSO based Relative Reduct (PSO-RR) and PSO based Quick Reduct (PSO-QR) are presented for the diseases diagnosis. The experimental result on several standard medical datasets proves the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques.