多标签分类的广义k-标签集集成

Hung-Yi Lo, Shou-de Lin, H. Wang
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引用次数: 3

摘要

标签功率集(LP)方法是多标签学习算法的一种。它通过将训练集中每个不同的标签组合视为不同的类别,将多标签分类问题简化为多类分类问题。本文提出了一种多标签分类的基展开模型,其中基函数是在随机k-标签集上训练的LP分类器。学习扩展系数以最小化预测与多标签真实值之间的全局误差。我们推导了一个解析解来有效地学习系数。我们使用几个基准数据集进行了实验,并将我们的方法与其他最先进的多标签学习方法进行了比较。结果表明,与其他方法相比,我们的方法具有更好或更具竞争力的性能。
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Generalized k-labelset ensemble for multi-label classification
Label powerset (LP) method is one category of multi-label learning algorithms. It reduces the multi-label classification problem to a multi-class classification problem by treating each distinct combination of labels in the training set as a different class. This paper proposes a basis expansion model for multi-label classification, where a basis function is a LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the multi-label ground truth. We derive an analytic solution to learn the coefficients efficiently. We have conducted experiments using several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. The results show that our method has better or competitive performance against other methods.
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