Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Significance of Gene Ranking for Classification of Microarray Samples
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bioinformatics
Microarray-based classification and clinical predictors
Bioinformatics
Multiclass classification and gene selection with a stochastic algorithm
Computational Statistics & Data Analysis
Support vector-based feature selection using Fisher's linear discriminant and Support Vector Machine
Expert Systems with Applications: An International Journal
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variable selection using random forests
Pattern Recognition Letters
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Expert Systems with Applications: An International Journal
Using partial least squares and support vector machines for bankruptcy prediction
Expert Systems with Applications: An International Journal
A General Wrapper Approach to Selection of Class-Dependent Features
IEEE Transactions on Neural Networks
PLS-Based Gene Selection and Identification of Tumor-Specific Genes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper focuses on high-dimensional and ultrahigh dimensional multi-category problems and presents a feature selection framework based on local recursive feature elimination (Local-RFE). Using this analytical framework, we propose a new feature selection algorithm, PLS-based local-RFE (LRFE-PLS). In order to compare the effectiveness of the proposed methodology, we also present PLS-based Global-RFE which takes all categories into consideration simultaneously. The advantage of the proposed algorithms lies in the fact that PLS-based feature ranking can quickly delete irrelevant features and RFE can concurrently remove some redundant features. As a result, the selected feature subset is more compact. In this paper the proposed algorithms are compared to some state-of-the-art methods using multiple datasets. Experimental results show that the proposed algorithms are competitive and work effectively for high-dimensional multi-category data. Statistical tests of significance show that LRFE-PLS algorithm has better performance. The proposed algorithms can be effectively applied not only to microarray data analysis but also to image recognition and financial data analysis.