A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets

2011 ◽  
Vol 32 (5) ◽  
pp. 701-711 ◽  
Author(s):  
Pablo Bermejo ◽  
Jose A. Gámez ◽  
Jose M. Puerta
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Hybrid Filter ◽  
High Dimensional Datasets

Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking

2012 ◽  
Vol 25 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Pablo Bermejo ◽  
Luis de la Ossa ◽  
José A. Gámez ◽  
José M. Puerta
Keyword(s):  
Subset Selection ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
High Dimensional Datasets

IMPROVING INCREMENTAL WRAPPER-BASED SUBSET SELECTION VIA REPLACEMENT AND EARLY STOPPING

2011 ◽  
Vol 25 (05) ◽  
pp. 605-625 ◽  
Author(s):  
PABLO BERMEJO ◽  
JOSE A. GAMEZ ◽  
JOSE M. PUERTA
Keyword(s):  
Subset Selection ◽  
Adaptive Threshold ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Early Stopping ◽  
Stopping Sets ◽  
Wrapper Approach ◽  
Selected Subset ◽  
High Dimensional Datasets

This paper deals with the problem of feature subset selection in classification-oriented datasets with a (very) large number of attributes. In such datasets complex classical wrapper approaches become intractable due to the high number of wrapper evaluations to be carried out. One way to alleviate this problem is to use the so-called filter-wrapper approach or Incremental Wrapper-based Subset Selection (IWSS), which consists of the construction of a ranking among the predictive attributes by using a filter measure, and then a wrapper approach is used by following the rank. In this way the number of wrapper evaluations is linear on the number of predictive attributes. In this paper we present two contributions to the IWSS approach. The first one is related with obtaining more compact subsets, and enables not only the addition of new attributes but also their interchange with some of those already included in the selected subset. Our second contribution, termed early stopping, sets an adaptive threshold on the number of attributes in the ranking to be considered. The advantages of these new approaches are analyzed both theoretically and experimentally. The results over a set of 12 high-dimensional datasets corroborate the success of our proposals.

scholarly journals Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Gürcan Yavuz ◽  
Doğan Aydin
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Continuous Optimization ◽  
Subset Selection ◽  
Machine Intelligence ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Bee Colony ◽  
Angle Modulation

Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets.

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