Scalable Feature Selection in High-Dimensional Data Based on GRASP |
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Authors: | Mohsen Moshki Peyman Kabiri Alireza Mohebalhojeh |
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Affiliation: | 1. Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iranmoshki@iust.ac.ir;3. Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran;4. Institute of Geophysics, University of Tehran, Tehran, Iran |
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Abstract: | Feature selection in high-dimensional data is one of the active areas of research in pattern recognition. Most of the algorithms in this area try to select a subset of features in a way to maximize the accuracy of classification regardless of the number of selected features that affect classification time. In this article, a new method for feature selection algorithm in high-dimensional data is proposed that can control the trade-off between accuracy and classification time. This method is based on a greedy metaheuristic algorithm called greedy randomized adaptive search procedure (GRASP). It uses an extended version of a simulated annealing (SA) algorithm for local search. In this version of SA, new parameters are embedded that allow the algorithm to control the trade-off between accuracy and classification time. Experimental results show supremacy of the proposed method over previous versions of GRASP for feature selection. Also, they show how the trade-off between accuracy and classification time is controllable by the parameters introduced in the proposed method. |
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