Paper Info
Title
Solving Feature Subset Selection Problem by a Hybrid Metaheuristic.
Abstract
Abstract The aim of this paper is to develop a hybrid metaheuristic based on Variable Neighbourhood Search and Tabu Search for solving the Feature Subset Selection Problem in classification. Given a set of instances characterized by several features, the classification problem consists of assigning a class to each instance. Feature Subset Selection Problem selects a relevant subset of features from the initial set in order to classify future instances. The proposed hybrid metaheuristic is compared with a Genetic Algorithm proposed in the literature. Although he hybrid metaheuristic and the genetic algorithm had a similar performance according to the accuracy percentages, the hybrid metaheuristic provided a higher reduction in the set of features.
Year
Venue
Keywords
2004
Hybrid Metaheuristics
tabu search,genetic algorithm
Field
DocType
Citations 
Guided Local Search,Computer science,Neighbourhood (mathematics),Artificial intelligence,Machine learning,Genetic algorithm,Tabu search,Metaheuristic
Conference
3
PageRank 
References 
Authors
0.40
20
5
Name
Order
Citations
PageRank
Miguel García-Torres131.75
Félix C. García López230.74
Belén Melián-batista337230.00
José A. Moreno-pérez464037.05
J. Marcos Moreno-vega546333.52