期刊
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
卷 16, 期 1, 页码 84-98出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MCI.2020.3039070
关键词
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资金
- Marsden Fund of New Zealand government [VUW1509, VUW1615]
- Science for Technological Innovation Challenge (SfTI) fund [E3603/2903]
- University Research Fund at Victoria Univer sity of Wellington [223805/3986]
- MBIE Data Science SSIF Fund [RTVU1914]
- National Natural Science Foundation of China (NSFC) [61876169, 61672276, 51975294]
- China Scholarship Council/Victoria University Scholarship
Cost-sensitive learning is a popular approach for addressing class imbalance in machine learning, but the proposed genetic programming-based approach in this paper shows promising results in developing cost-sensitive classifiers independent of manually designed cost matrices. Experiment results on high-dimensional unbalanced datasets demonstrate the effectiveness of this approach.
Cost-sensitive learning is a popular approach to addressing the problem of class imbalance for many classification algorithms in machine learning. However, most cost-sensitive algorithms are dependent on manually designed cost matrices. Unfortunately, in many cases, it is often not easy for humans, even experts, to accurately specify misclassification costs for different mistakes due to the lack of domain knowledge related to actual situations in some complex unbalanced problems. As a result, these cost-sensitive algorithms cannot be directly applied. This paper proposes a new genetic programmingbased approach to developing cost-sensitive classifiers that are independent of manually designed cost matrices. The proposed method is able to construct classifiers and learn cost intervals automatically and simultaneously. In the proposed method, a tree representation, terminal sets and function sets are designed and developed. We examine the effectiveness of the proposed method on ten high-dimensional unbalanced datasets. The experimental results show that the proposed method often outperforms compared methods for highdimensional unbalanced classification. Furthermore, according to the analysis of evolved trees, the constructed classifiers often only need a small number of features to achieve a good classification performance.
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