自动化学报2011，Vol.37Issue(8)：973-983,11.DOI:10.3724/SP.J.1004.2011.00973
多分类问题代价敏感AdaBoost算法
Cost-sensitive AdaBoost Algorithm for Multi-class Classification Problems
摘要
Abstract
To solve the cost merging problem when multi-class cost-sensitive classification is transferred to two-class cost-sensitive classification, a cost-sensitive AdaBoost algorithm which can be applied directly to multi-class classification is constructed. The proposed algorithm is similar to real AdaBoost algorithm in algorithm flow and error estimation formula. When the costs are equal, this algorithm becomes a new real AdaBoost algorithm for multi-class classification, guaranteeing that the training error of the combination classifier could be reduced while the number of trained classifiers increased. The new real AdaBoost algorithm does not need to meet the condition that every classifier must be independent, that is to say, the independent condition of classifiers can be derived from the new algorithm, instead of being the must for current real AdaBoost algorithm for multi-class classification. The experimental results show that this new algorithm always ensures the classification result trends to the class with the smallest cost, while the existing multi-class cost-sensitive learning algorithm may fail if the costs of being erroneously classified to other classes are imbalanced and the average cost of every class is equal. The research method above provides a new idea to construct new ensemble learning algorithms, and an AdaBoost algorithm for multi-label classification is given, which is easy to operate and approximately meets the smallest error classification rate.关键词
代价敏感学习/多分类问题/多标签分类问题/连续AdaBoost/代价敏感分类Key words
Cost-sensitive learning, multi-class classification problem, multi-label classification problem, real AdaBoost, cost-sensitive classification引用本文复制引用
付忠良..多分类问题代价敏感AdaBoost算法[J].自动化学报,2011,37(8):973-983,11.基金项目
国家高技术研究发展计划(863计划)(2008AAO1Z402),四川省科技支撑计划项目 (2008SZ0100,2009SZ0214)资助 （863计划）
