An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem

摘要

The minimum number attribute reduction problem is an important issue when dealing with huge amounts of data. The problem of minimum attribute reduction is formally known to be as an NP complete nonlinearly constrained optimization problem. Social spider optimization algorithm is a new meta-heuristic algorithm of the swarm intelligence field to global solution. The social spider optimization algorithm is emulates the behavior of cooperation between spiders based on the biological laws of the cooperative colony. Inspired by the social spiders, in this paper, an improved social spider algorithm for the minimal reduction problem was proposed. In the proposed algorithm, the fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. For each spider, the fitness function is computed and compared with the global best fitness value. If the current value is better, then the global best fitness is replaced with it and its position became the reduct set. Then, the position of each spider is updated according to its type. This process is repeated until the stopping criterion is satisfied. To validate the proposed algorithm, several real clinical medical datasets which are available from the UCI Machine Learning Repository were used to compute the performance of the proposed algorithm. The experimental results illustrate that the proposed algorithm is superior to state-of-the-art swarm-based in terms of classification accuracy while limiting number of features.