Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness.

Investigation of several algorithms for computing exact minimax values of game trees (utilizing backward pruning) are discussed. The focus is on trees with an ordering similar to that actually found in game playing practice. The authors compare the algorithms using two different distributions of the static values, the uniform distribution and a distribution estimated from practical data. A systematic comparison of using aspiration windows for all of the usual minimax algorithms is presented. The effects of aspiration windows of varying size and position are analyzed. Increasing the ordering of moves to near the optimum results in unexpectedly high savings. Algorithms with linear space complexity benefit most. Although the ordering of the first move is of predominant importance, that of the remainder has only second-order effects. The use of an aspiration window not only makes alpha-beta search competitive, but there also exist dependencies of its effects on certain properties of the trees.>