Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.
Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Hangzhou, China.
Comput Intell Neurosci. 2018 May 6;2018:5078268. doi: 10.1155/2018/5078268. eCollection 2018.
The paper presents a novel approach for feature selection based on extreme learning machine (ELM) and Fractional-order Darwinian particle swarm optimization (FODPSO) for regression problems. The proposed method constructs a fitness function by calculating mean square error (MSE) acquired from ELM. And the optimal solution of the fitness function is searched by an improved particle swarm optimization, FODPSO. In order to evaluate the performance of the proposed method, comparative experiments with other relative methods are conducted in seven public datasets. The proposed method obtains six lowest MSE values among all the comparative methods. Experimental results demonstrate that the proposed method has the superiority of getting lower MSE with the same scale of feature subset or requiring smaller scale of feature subset for similar MSE.
本文提出了一种基于极限学习机（ELM）和分数阶达尔文粒子群优化（FODPSO）的特征选择新方法，用于回归问题。该方法通过计算 ELM 获得的均方误差（MSE）来构建适应度函数。并且通过改进的粒子群优化，FODPSO 来搜索适应度函数的最优解。为了评估所提出方法的性能，在七个公共数据集上与其他相关方法进行了比较实验。在所比较的方法中，该方法获得了六个最低的 MSE 值。实验结果表明，该方法具有在相同特征子集规模下获得更低 MSE 或在相似 MSE 下需要更小特征子集规模的优势。
