Title: Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification

Authors: Huijuan Lu; Shasha Wei; Zili Zhou; Yanzi Miao; Yi Lu

Addresses: College of Information Engineering, China Jiliang University, Hangzhou 310018, China ' College of Information Engineering, China Jiliang University, Hangzhou 310018, China ' College of Computer Engineering, Zhejiang Institute of Mechanical & Engineering, Hangzhou 310053, China ' School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China ' Department of Computer Science, Prairie View A&M University, Prairie View 77446, USA

Abstract: The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Keywords: regularised extreme learning machine; misclassification cost; rejection cost; gene expression data; data classification; bioinformatics; colon tumours; colon cancer.

DOI: 10.1504/IJDMB.2015.069657

International Journal of Data Mining and Bioinformatics, 2015 Vol.12 No.3, pp.294 - 312

Received: 27 Feb 2014
Accepted: 22 Mar 2014
Published online: 29 May 2015 *

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