回归核极限学习机的多标记学习算法

基于极限学习机(ELM)的多标记学习算法多使用ELM分类模式,忽略标记之间存在的相关性.为此,文中提出结合关联规则与回归核极限学习机的多标记学习算法(ML-ASRKELM).首先通过关联规则分析标记空间,提取标记之间的规则向量.然后通过提出的多标记回归核极限学习机(ML-RKELM)得出预测结果.若规则向量不为空,将规则向量与预测结果运算得出最终预测结果,否则最终结果即为ML-RKELM的预测结果.对比实验表明ML-ASRKELM与ML-RKELM性能较优,统计假设检验进一步说明文中算法的有效性.

Multi-label Learning Algorithm of Regression Kernel Extreme Learning Machine

In the multi-label learning algorithms based on extreme learning machine(ELM), the ELM classification model is often used, and the correlation between labels is ignored. Accordingly, a multi-label learning algorithm of regression kernel extreme learning machine with association rules(ML-ASRKELM) is proposed in this paper. Firstly, the rule vectors between labels are extracted by analyzing the association rules of label space. Then, the prediction results are obtained by the proposed multi-label regression kernel extreme learning machine(ML-RKELM). Eventually, if the rule vectors are not empty, the final results are calculated by the rule vectors and the prediction results of ML-KRELM. Otherwise, the final results are predicted by ML-RKELM. The experimental results show that ML-ASRKELM and ML-RKELM are superior to other algorithms, and the effectiveness of the proposed algorithms are illustrated by the statistical hypothesis test.