Performance of feature-selection methods in the classification of high-dimension data

2009 ◽  
Vol 42 (3) ◽  
pp. 409-424 ◽  
Author(s):  
Jianping Hua ◽  
Waibhav D. Tembe ◽  
Edward R. Dougherty
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohammed Qaraad ◽  
Souad Amjad ◽  
Ibrahim I.M. Manhrawy ◽  
Hanaa Fathi ◽  
Bayoumi A. Hassan ◽  
...  

2015 ◽  
Vol 1 (311) ◽  
Author(s):  
Katarzyna Stąpor

Discriminant Analysis can best be defined as a technique which allows the classification of an individual into several dictinctive populations on the basis of a set of measurements. Stepwise discriminant analysis (SDA) is concerned with selecting the most important variables whilst retaining the highest discrimination power possible. The process of selecting a smaller number of variables is often necessary for a variety number of reasons. In the existing statistical software packages SDA is based on the classic feature selection methods. Many problems with such stepwise procedures have been identified. In this work the new method based on the metaheuristic strategy tabu search will be presented together with the experimental results conducted on the selected benchmark datasets. The results are promising.


2017 ◽  
Vol 222 ◽  
pp. 49-56 ◽  
Author(s):  
Lucas R. Trambaiolli ◽  
Claudinei E. Biazoli ◽  
Joana B. Balardin ◽  
Marcelo Q. Hoexter ◽  
João R. Sato

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