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Feature Selection Methods using Meta Heuristics


Manu Singla , University Institute of Engineering and Technology; Poonam Dabas, University Institute of Engineering and Technology


Data Mining, Feature Selection, Multi-Objective Optimization, Meta Heuristics


The Feature Selection approaches can generally be divided into three groups: filter, wrapper, and hybrid approaches. The filter approach operates independently of any learning algorithm. These methods rank the features by some criteria and omit all features that do not achieve a sufficient score. Due to its computational efficiency, the filter methods are very popular to high-dimension data. Traditional search and optimization methods such as gradient-based methods are difficult to extend to the multi objective case because their basic design precludes the consideration of multiple solutions. In contrast, population-based methods such as evolutionary algorithms are well-suited for handling such situations. There are different approaches for solving multi objective optimization problems. For a large number of features, evaluating all states is computationally non-feasible requiring heuristic search methods. More recently, nature inspired metaheuristic algorithms have been used to select features, namely: particle swarm optimization (PSO), genetic algorithm (GA)-based attribute reduction, gravitational search algorithm (GSA). These methods attempt to achieve better solutions by application of knowledge from previous iterations. In this paper we survey feature selection and various Feature selection related to multi-objective optimization using meta heuristics.

Other Details

Paper ID: IJSRDV4I50578
Published in: Volume : 4, Issue : 5
Publication Date: 01/08/2016
Page(s): 878-884

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