Enhancement of ELM by Clustering Discrimination Manifold Regularization and Multiobjective FOA for Semisupervised Classification

Qing Ye,Hao Pan,Changhua Liu

doi:10.1155/2015/731494

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.

Highlights

extreme learning machine (ELM) [1, 2] shows better performance than traditional gradient-based learning methods and support vector machine (SVM) [3, 4] in regression and classification applications due to its faster learning capacity
Computational Intelligence and Neuroscience paper develops an improved variant of Fly Optimization Algorithm (FOA) named multiobjective fruit fly optimization algorithm (MOFOA) to optimize crucial parameters of clustering discrimination manifold regularization (CDMR)-ELM consisting of the number of hidden nodes and trade-off parameters for further improving the classification accuracy and efficiency
In order to evaluate the accuracy and efficiency of the proposed MOFOA-CDMRELM classifier, we perform a set of experiments on several real-world datasets from the UCI machine learning repository and benchmark repository frequently used for semisupervised learning [23]

Summary

Introduction

ELM [1, 2] shows better performance than traditional gradient-based learning methods and support vector machine (SVM) [3, 4] in regression and classification applications due to its faster learning capacity. Wu et al [11] proposed semisupervised discrimination regularization (SSDR) for solving misclassification by utilizing discrimination of labeled data in learning. An improved MR framework named clustering discrimination manifold regularization (CDMR) which integrates clustering discrimination of both labeled and unlabeled data with twinning constraints regularization is proposed, and a semisupervised ELM with CDMR framework termed CDMR-ELM is developed. The proposed novel framework can effectively avoid boundary misclassification which frequently occurred in manifold regularization and improve the classification accuracy by combining the clustering discrimination with twinning constraints regularization containing lower intracluster compactness and higher intercluster separability. Computational Intelligence and Neuroscience paper develops an improved variant of FOA named multiobjective fruit fly optimization algorithm (MOFOA) to optimize crucial parameters of CDMR-ELM consisting of the number of hidden nodes and trade-off parameters for further improving the classification accuracy and efficiency.

Related Basic Theory

The Proposed Classifier

Experiment Results and Discussion

Conclusion