rEDA: reverse estimation of distribution algorithm for classification

Estimation of distribution algorithm EDA is a branch of evolutionary algorithms. EDA replaces recombination and mutation operators with the estimation of probabilistic distribution of selected individuals. However, these selected individuals only cover part of the problem to be optimised, which causes that the algorithm may easily fall into a local optimum. In this paper, we propose a variation of EDA, reverse estimation of distribution algorithm rEDA, from the perspective of reverse process. Different from the EDA process that individuals are firstly given and then the estimation of models starts, rEDA is to firstly give initial models and then regulate these models relying on sampling from the models and optimisation objective. We employ rEDA to classification in data mining area and propose a novel classification algorithm based on rEDA. The proposed rEDA algorithm and rEDA-based classification algorithm are analysed theoretically. The empirical results show our proposed algorithm outperforms some classical classification algorithms in accuracy.

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