基于不同权重的多标签分类器准确性评估方法

分类问题是数据挖掘领域的研究热点之一。多标签分类器可以将数据对象预测为多个类别，训练集中属性相同但对应类标签不同的对象的数目是不平衡的，而现有的评估算法并未能区分其代价。提出了一种基于不同权重的准确性评估方法EMOWDIF，根据多标签数据对象属于相同属性不同类别的数目之间的比值计算相应的权重，对分类器模型给予不同程度的奖惩，从而区分不同分类器的性能。方法用编程实现，并对多标签数据集的分类结果进行评估。实验结果表明该方法能有效评估分类器。

Weights-based accuracy evaluation method for multi-label classifier

Classification is one of the hotspots of data mining.The multi-label classifier can predict a set of labels for one data object.Data objects which have same attributes but their corresponding labels are different,and the number of them is imbalanced.The existing evaluation algorithm can not distinguish the cost.A weights-based accuracy evaluation method EMOWDIF（Evaluation Method Based on Weights Difference） is proposed.The weight is calculated according to the ratio of the multi-label instances,giving different rewards to the classifier,and can distinguish the performance of different classifiers effectively.It is programmed and used to evaluate the classification result.Experiments show the mothod can get a better performance on evaluating the classifier.