Proceedings ArticleDOI
A hybrid feature selection method for high-dimensional data
Nooshin Taheri,Hossein Nezamabadi-pour +1 more
- pp 141-145
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TLDR
An ensemble of three different filter ranking methods including: Information Gain, ReliefF and F-score are used to reduce the dimension of datasets and the experimental results confirm the capability of the proposedIBGSA.Abstract:
Feature selection is one of the important preprocessing steps in analyzing high dimensional datasets. In this paper, first the ensemble of three different filter ranking methods including: Information Gain (IG), ReliefF and F-score are used to reduce the dimension of datasets. Afterward, reduced data are utilized as inputs of the meta-heuristic algorithm, Improved Binary Gravitational Search Algorithm (IBGSA), for selecting optimal subset of features with highest classification accuracy rate. In order to evaluate the proposed method, it is applied to several high-dimension standard datasets and the results in terms of classification accuracy and feature reduction rate are presented. The experimental results confirm the capability of the proposed algorithm.read more
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References
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Journal ArticleDOI
GSA: A Gravitational Search Algorithm
TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
Journal ArticleDOI
BGSA: binary gravitational search algorithm
TL;DR: A binary version of the gravitational search algorithm, based on the law of gravity and mass interactions, is introduced and the experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions.
Journal ArticleDOI
A review of microarray datasets and applied feature selection methods
Verónica Bolón-Canedo,Noelia Sánchez-Maroño,Amparo Alonso-Betanzos,José Manuel Benítez,Francisco Herrera,Francisco Herrera +5 more
TL;DR: An experimental evaluation on the most representative datasets using well-known feature selection methods is presented, bearing in mind that the aim is not to provide the best feature selection method, but to facilitate their comparative study by the research community.
Journal ArticleDOI
A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm
TL;DR: Two-stage feature selection and feature extraction is used to improve the performance of text categorization and the proposed model is able to achieve high categorization effectiveness as measured by precision, recall and F-measure.
Journal ArticleDOI
Hybrid feature selection by combining filters and wrappers
TL;DR: A hybrid feature selection method which combines two feature selection methods - the filters and the wrappers is introduced, which shows that equal or better prediction accuracy can be achieved with a smaller feature set.
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