关键词: 离散型增强烟花算法, 特征选择, 降维, 分类, [k]近邻[(kNN)]

Abstract:

Feature selection is to select feature subsets from the original feature set, and it can reduce the dimension of feature and redundant information, so as to improve the accuracy of classification. In order to achieve this effect, a new feature selection algorithm is proposed in this paper. The algorithm uses the enhanced fireworks algorithm after discretization to search the feature subset. At the same time, the feature subset and the constraint conditions after penalty factor processing are integrated into the objective function. Then the data of the feature subset are trained and predicted by the [kNN] classifier. Finally, the accuracy of classification is tested by 10-fold cross validation. Compared with the guided fireworks algorithm, fireworks algorithm, bat algorithm, crow search algorithm and adaptive particle swarm optimization algorithm, the simulation results using UCI data show that the overall performance of the proposed algorithm is better than that of the other five algorithms.

Key words: discrete enhanced fireworks algorithm, feature selection, dimension reduction, classification, [k]-Nearest Neighbor[(kNN)]