Feature Selection Based on Asynchronous Discrete Particle Swarm Optimal Search Algorithm

Hsieh, Wen-Ting; Horng, Shi–Jinn

doi:10.1109/paap.2012.44

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Chi et al presented a method for continuous attribute discretization based on quantum PSO algorithm [ 14 ]. Hsieh and Horng presented a method for feature selection based on asynchronous discrete PSO search algorithm [ 15 ]. Also, other stochastic algorithms were used to attribute reduction, for example, ant colony algorithm (ACO) [ 16 ] and support vector machine (SVR) [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A Simple Fitness Function for Minimum Attribute Reduction

Yue-bin

Guo

2015

Computational Intelligence and Neuroscience

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The goal of minimal attribute reduction is to find the minimal subset R of the condition attribute set C such that R has the same classification quality as C. This problem is well known to be NP-hard. When only one minimal attribute reduction is required, it was transformed into a nonlinearly constrained combinatorial optimization problem over a Boolean space and some heuristic search approaches were used. In this case, the fitness function is one of the keys of this problem. It required that the fitness function must satisfy the equivalence between the optimal solution and the minimal attribute reduction. Unfortunately, the existing fitness functions either do not meet the equivalence, or are too complicated. In this paper, a simple and better fitness function based on positive domain was given. Theoretical proof shows that the optimal solution is equivalent to minimal attribute reduction. Experimental results show that the proposed fitness function is better than the existing fitness function for each algorithm in test.

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Section: Introductionmentioning

confidence: 99%