Journal Article10.1142/S0129054115500185
Novel Randomized Feature Selection Algorithms
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TL;DR: Three randomized algorithms for feature selection are presented that are generic in nature and can be applied for any learning algorithm.
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Abstract: Feature selection is the problem of identifying a subset of the most relevant features in the context of model construction. This problem has been well studied and plays a vital role in machine learning. In this paper we present three randomized algorithms for feature selection. They are generic in nature and can be applied for any learning algorithm. Proposed algorithms can be thought of as a random walk in the space of all possible subsets of the features. We demonstrate the generality of our approaches using three different applications. The simulation results show that our feature selection algorithms outperforms some of the best known algorithms existing in the current literature.
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Citations
Novel and Efficient Randomized Algorithms for Feature Selection
Zigeng Wang,Xia Xiao,Sanguthevar Rajasekaran +2 more
- 16 Jul 2020
TL;DR: This paper proposes automatic breadth searching and attention searching adjustment approaches to further speedup randomized wrapper based feature selection and shows that, compared with existing approaches, these techniques can locate a more meaningful set of features with a high efficiency.
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31
A Feature Selection and Classification Algorithm Based on Randomized Extraction of Model Populations
TL;DR: A novel classification approach adopted from the nonlinear model identification framework, which jointly addresses the feature selection and classifier design tasks, and is compared to other well-known FS and classification methods on standard benchmark problems.
A robust and stable gene selection algorithm based on graph theory and machine learning.
TL;DR: In this paper, a robust and stable supervised gene selection algorithm is proposed to select a set of robust genes having a better prediction ability from the gene expression datasets with phenotypes, which is ensured by class and instance level perturbations, respectively.
4
Efficient Randomized Feature Selection Algorithms
Zigeng Wang,Sanguthevar Rajasekaran +1 more
- 01 Aug 2019
TL;DR: This paper presents efficient randomized feature selection algorithms empowered by automatic breadth searching and attention searching adjustments that achieve significant improvements in the selected features' quality and selection time.
3
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