计算机科学
机器学习
一级分类
人工智能
适应性
统计分类
二元分类
核(代数)
数据挖掘
班级(哲学)
比例(比率)
支持向量机
数学
生态学
组合数学
生物
物理
量子力学
作者
Yingying Chen,Zijie Hong,Xiaowei Yang
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2023-10-01
卷期号:35 (10): 10554-10568
标识
DOI:10.1109/tkde.2023.3266648
摘要
Imbalanced classification is a challenging task in the fields of machine learning, data mining and pattern recognition. Cost-sensitive online algorithms are very important methods for large-scale imbalanced classification problems. At present, most of the cost-sensitive classification algorithms focus on the accuracy of the minority class and ignore the accuracy of the majority class. In order to better balance the accuracy between the minority class and the majority class, in this article, a misclassification cost is presented to ensure that the cost-sensitive online algorithm can better deal with the imbalanced classification problems without signifificantly reducing the accuracy of the majority class. Based on the proposed misclassification cost, a novel cost-sensitive online adaptive kernel learning algorithm is proposed to boost the adaptability of kernel function when data arrives one by one. According to the essential characteristics of the imbalanced binary classification, a cost-sensitive online adaptive kernel learning algorithm is given to handle the large-scale imbalanced multi-class classification problems. Theoretical analysis of the proposed algorithms are provided. Extensive experiments demonstrate that compared with the state-of-the-art imbalanced classification algorithms, the proposed algorithms can significantly improve the classification performances on most of the large-scale imbalanced data sets.
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