An ensemble learning algorithm based on Ranking Loss was proposed to solve the mutil-label classification problem that the instance may belong to several classes at the same time.The algorithm based on the key idea of the real AdaBoost algorithm was proposed from the definition of Ranking Loss.It aimed to minimize the sample space.The algorithm trained weak classifiers by using iterative method,then integrated these weak classifiers to a strong classifier.The Ranking Loss of the algorithm would be gradually reduced as the number of weak classifiers increased.And the algorithm steps were given.Theoretical analysis and experimental results show that this ensemble learning algorithm is effective and stable.%针对目标可以属于多个类别的多标签分类问题,提出了一种基于Ranking Loss最小化的集成学习方法.算法基于Real AdaBoost算法的核心思想,从Ranking Loss定义出发,以Ranking Loss在样本空间最小化为目标,采取迭代的方法训练多个弱分类器,并将这些弱分类器集成起来构成强分类器,强分类器的Ranking Loss随着弱分类器个数的增加而逐渐减少,并给出了算法流程.通过理论分析和实验数据对比验证了提出的多标签分类算法的有效性和稳定性.
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