title: Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification.
abstract: A novel semisupervised extreme learning machine (ELM) with clustering discrimination manifold regularization (CDMR) framework named CDMR-ELM is proposed for semisupervised classification. By using unsupervised fuzzy clustering method, CDMR framework integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization. Aiming at further improving the classification accuracy and efficiency, a new multiobjective fruit fly optimization algorithm (MOFOA) is developed to optimize crucial parameters of CDME-ELM. The proposed MOFOA is implemented with two objectives: simultaneously minimizing the number of hidden nodes and mean square error (MSE). The results of experiments on actual datasets show that the proposed semisupervised classifier can obtain better accuracy and efficiency with relatively few hidden nodes compared with other state-of-the-art classifiers.
authors.affiliation: School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430000, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Ye
authors.affiliation: School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430000, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Pan
authors.affiliation: Yangtze University College of Technology and Engineering, Jingzhou 430023, China.
authors.forename: Changhua
authors.initials: C
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016002:Discriminant Analysis; D016571:Neural Networks, Computer; D000069553:Supervised Machine Learning