计算机工程与应用2020，Vol.56Issue(1)：172-179,8.DOI:10.3778/j.issn.1002-8331.1809-0049
基于改进花粉算法的极限学习机分类模型
Improved Flower Pollination Algorithm Extreme Learning Machine Classification Model
摘要
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 Algo-rithm(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 experi-ment, the effectiveness of the CS-ACFPA algorithm for the improvement of the MELM model is verified, and the advan-tages of the CS-ACFPA-MELM model on large-scale samples and the applicability of small samples are demonstrated.关键词
分类模型/极限学习机/花粉算法/代价敏感/混沌搜索Key words
classification model/extreme learning machine/pollen algorithm/cost sensitive/chaotic search分类
信息技术与安全科学引用本文复制引用
邵良杉,李臣浩..基于改进花粉算法的极限学习机分类模型[J].计算机工程与应用,2020,56(1):172-179,8.基金项目
国家自然科学基金(No.71771111). （No.71771111）
