Instance Selection Using Evolutionary Algorithms: An Experimental Study

2007 ◽  
pp. 127-152 ◽  
Author(s):  
José Ramón Cano ◽  
Francisco Herrera ◽  
Manuel Lozano
Keyword(s):  
Experimental Study ◽  
Evolutionary Algorithms ◽  
Instance Selection

scholarly journals An experimental study of adaptive control for evolutionary algorithms

2015 ◽  
Vol 35 ◽  
pp. 359-372 ◽  
Author(s):  
Giacomo di Tollo ◽  
Frédéric Lardeux ◽  
Jorge Maturana ◽  
Frédéric Saubion
Keyword(s):  
Experimental Study ◽  
Adaptive Control ◽  
Evolutionary Algorithms

A Survey on Evolutionary Instance Selection and Generation

2010 ◽  
Vol 1 (1) ◽  
pp. 60-92 ◽  
Author(s):  
Joaquín Derrac ◽  
Salvador García ◽  
Francisco Herrera
Keyword(s):  
Data Mining ◽  
Evolutionary Algorithms ◽  
Nearest Neighbor ◽  
Instance Selection ◽  
Data Sets ◽  
Generation Process ◽  
Data Mining Algorithms ◽  
Instance Generation ◽  
Nearest Neighbor Rule ◽  
Mining Algorithms

The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

A Survey on Evolutionary Instance Selection and Generation

2012 ◽  
pp. 233-266 ◽  
Author(s):  
Joaquín Derrac ◽  
Salvador García ◽  
Francisco Herrera
Keyword(s):  
Data Mining ◽  
Evolutionary Algorithms ◽  
Nearest Neighbor ◽  
Instance Selection ◽  
Data Sets ◽  
Generation Process ◽  
Data Mining Algorithms ◽  
Instance Generation ◽  
Nearest Neighbor Rule ◽  
Mining Algorithms

The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification

2009 ◽  
Vol 179 (23) ◽  
pp. 3998-4028 ◽  
Author(s):  
Oscar Ibáñez ◽  
Lucia Ballerini ◽  
Oscar Cordón ◽  
Sergio Damas ◽  
José Santamaría
Keyword(s):  
Experimental Study ◽  
Evolutionary Algorithms ◽  
Forensic Identification
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