Calibrated Multi-label Classification with Label Correlations
Calibrated Multi-label Classification with Label Correlations
复制标题
具有标签相关性的校准多标签分类
DOI：
10.1007/s11063-018-9925-2
复制
发表时间：
--
影响因子：
3.1
通讯作者：
MingYang
中科院分区：
文献类型：
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作者：
Zhifen He;MingYang
多标签分类是一种特殊的学习任务，每个实例可能同时与多个标签相关联。存在两个主要挑战：(A)自动发现和利用标签相关性，以及(B)有效地将每个实例的相关标签与无关标签分离。然而，许多现有的多标签分类算法不能同时处理这两个挑战。本文将多标签分类、标签相关性和阈值校准结合到一个统一的学习框架中，提出了基于标签相关性的校准多标签分类算法CMLLC。具体地，我们首先引入标签协方差矩阵来刻画标签之间的相关性，并引入虚拟标签来校准每个实例的标签判决阈值。其次，构建了CMLLC模型的框架，用于联合学习每个标签和虚拟标签对应的标签相关性和模型参数。最后，优化问题是联合凸的，并用交替迭代法求解。在16个多标签基准数据集上的5种评价标准下的实验结果表明，CMLLC算法的性能优于现有的多标签分类算法。
Multi-label classification is a special learning task where each instance may be associated with multiple labels simultaneously. There are two main challenges: (a) discovering and exploiting the label correlations automatically, and (b) separating the relevant labels from the irrelevant labels of each instance effectively. Nevertheless, many existing multi-label classification algorithms fail to deal with both challenges at the same time. In this paper, we integrate multi-label classification, label correlations and threshold calibration into a unified learning framework, and propose calibrated multi-label classification with label correlations, named CMLLC. Specifically, we firstly introduce a label covariance matrix to characterize the label correlations and a virtual label to calibrate label decision threshold of each instance. Secondly, the framework of our CMLLC model is constructed for joint learning of the label correlations and model parameters corresponding to each label and the virtual label. Lastly, the optimization problem is jointly convex and solved by an alternating iterative method. Experimental results on sixteen multi-label benchmark datasets in terms of five evaluation criteria demonstrate that CMLLC outperforms the state-of-the-art multi-label classification algorithms.