Learning misclassification costs for imbalanced classification on gene expression data.

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ID: 72340
2019
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Abstract
Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights.In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches.Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.
Reference Key
lu2019learningbmc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lu, Huijuan;Xu, Yige;Ye, Minchao;Yan, Ke;Gao, Zhigang;Jin, Qun;
Journal BMC Bioinformatics
Year 2019
DOI
10.1186/s12859-019-3255-x
URL
Keywords
cost-sensitive misclassification cost parameter fitting weighted classification accuracy

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