Abstract
BackgroundCost-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.ResultsIn 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.ConclusionsComprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.
Highlights
Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem
Optimal cost weights searching From the University of California Irvine (UCI) standard classification dataset, we choose Leukemia, Colon, Prostate, Lung and Ovarian gene as the datasets for cost weights searching and further test, i.e., the Leukemia cancer dataset, the Colon cancer dataset, the Prostate cancer dataset, the Lung cancer dataset, and the Ovarian cancer in the tumor data respectively
Optimal cost weights searching by function fitting In this subsection, we use w and p as independent variables, and define a function fitting problem as: wc 1⁄4 f ðw; pÞ
Summary
Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. The characteristic of imbalanced data is serious imbalance in the proportion of positive and negative samples [5, 6]. The classification of gene expression data is a cost-sensitive problem, both positive and negative classifications of Traditional machine learning algorithms usually assume that the training set is balanced. For imbalanced datasets, such as the gene expression datasets, the classical classification algorithms with the correct classification rates (CCR) may bias towards the majority classes. The introduction of cost sensitive learning (CSL) is necessary to eliminate the defects of traditional classification algorithms for imbalanced datasets. We utilize a more sophisticated way to search for the optimal weights, and the proposed methods are more advanced than ever
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