Journal ArticleDOI
mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification
TLDR
This hybrid algorithm, called maximum relevance minimum redundancy PSO (mr^2PSO), is novel in the sense that it uses the mutual information available from the filter model to weigh the bit selection probabilities in the discrete PSO.About:
This article is published in Information Sciences.The article was published on 2011-10-01. It has received 267 citations till now. The article focuses on the topics: Minimum redundancy feature selection & Feature selection.read more
Citations
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Journal ArticleDOI
A Survey on Evolutionary Computation Approaches to Feature Selection
TL;DR: This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms.
Proceedings ArticleDOI
Feature selection based on mutual information
TL;DR: Experimental results indicate that the proposed feature selection based on mutual information criterion is capable of improving the performance of the machine learning models in terms of prediction accuracy and reduction in training time.
Journal ArticleDOI
A Survey on semi-supervised feature selection methods
TL;DR: In this paper, semi-supervised feature selection methods are fully investigated and two taxonomies of these methods are presented based on two different perspectives which represent the hierarchical structure of semi- supervised feature Selection methods.
Journal ArticleDOI
A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms
TL;DR: The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently and can easily identify and classify people with heart disease from healthy people.
Journal ArticleDOI
An unsupervised feature selection algorithm based on ant colony optimization
TL;DR: This paper presents an unsupervised feature selection method based on ant colony optimization, called UFSACO, which seeks to find the optimal feature subset through several iterations without using any learning algorithms.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
A Coefficient of agreement for nominal Scales
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Proceedings Article
A study of cross-validation and bootstrap for accuracy estimation and model selection
TL;DR: The results indicate that for real-word datasets similar to the authors', the best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.