关键词: 分类模型, 极限学习机, 花粉算法, 代价敏感, 混沌搜索

Abstract: Aiming at the problem of classification accuracy fluctuation caused by input layer weight and threshold random selection of Multi-output Extreme Learning Machine（MELM） classification model, a multi-classification model of extreme learning machine based on improved Flower Pollination Algorithm（CS-ACFPA） is proposed（CS-ACFPA-MELM）. Firstly, the adaptive strategy and Tent strategy are used to optimize the optimization method of Flower Pollination Algorithm（FPA）. Then a cost-sensitive fitness function is constructed to make the FPA better match the output of the MELM model. Finally, the improved FPA and the cost-sensitive fitness function are used to optimize the input weight and threshold of the extreme learning machine to improve the classification performance of the MELM model. In the contrast experiment, the effectiveness of the CS-ACFPA algorithm for the improvement of the MELM model is verified, and the advantages of the CS-ACFPA-MELM model on large-scale samples and the applicability of small samples are demonstrated.

Key words: classification model, extreme learning machine, pollen algorithm, cost sensitive, chaotic search