Learning misclassification costs for imbalanced classification on gene expression data

Huijuan Lu, Yige Xu, Minchao Ye*, Ke Yan, Zhigang Gao, Qun Jin

*この研究の対応する著者

研究成果: Article査読

11 被引用数 (Scopus)

抄録

Background: 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. Results: 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. Conclusions: Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.

本文言語English
論文番号681
ジャーナルBMC Bioinformatics
20
DOI
出版ステータスPublished - 2019 12 24

ASJC Scopus subject areas

  • 構造生物学
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 応用数学

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