Empirical evaluation of ensemble feature subset selection methods for learning from a high-dimensional database in drug design

2003 ◽  
Author(s):  
H. Mamitsuka
Keyword(s):  
Drug Design ◽  
Empirical Evaluation ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
High Dimensional ◽  
Feature Subset ◽  
Selection Methods

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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