Tumor Microarray Gene Expression Data Classification Based on Weighted Extreme Learning Machine

doi:10.12068/j.issn.1005-3026.2017.06.008

Abstract

Abstract:

With the development of gene microarray technology， gene expression profiling becomes a significant method for identifying different types of canners. Microarray gene expression data is from clinical trials in general， where the class distribution of samples is changeable， which makes the expression data have a chance to become more imbalanced. In this paper， the weighted extreme learning machine (WELM) was used to classify the imbalance microarray gene expressing data. In order to reduce classification error caused by the imbalance data， a weight was assigned to each sample in order to enhance the impact of minority class while reducing majority class’s impact， and improve the accuracy of tumor classification. The experimental results show that the minority class recognition rate can be well improved by the proposed method， so as to improve the overall performance of classifiers.

Key words: gene, microarray expressing data, WELM, imbalance, tumor classification

CLC Number:

TP391.4

JIANG Lin-ying， YU Dong-hai， SHI Xin. Tumor Microarray Gene Expression Data Classification Based on Weighted Extreme Learning Machine[J]. Journal of Northeastern University Natural Science, 2017, 38(6): 798-803.

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