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StaC: Stacked chaining for multi-label classification
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AI
DOI：10.1016/j.eswa.2023.119699.png)
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Abstract
En 中文
Multi-label classification has a fundamental challenge of finding the interdependence of labels. Stacking is one of the most prominent methods for the same. It uses the full dependency, i.e., it utilizes dependence among all the labels. The stacking method uses classifiers in two layers, where the first layer's classifiers assume no interdependence among labels. It leads to the first layer classifiers being error-prone. Also, it diminishes the performance of the second layer's classifiers because they are fed with the first layer's classifiers' outputs. Further, there is no correlation utilization among the second layer's classifiers. This paper addresses the poor performance of the first layer's classifiers and no correlation utilization at the second layer. It proposes a method called Stacked Chaining (StaC) that uses interdependence of labels in a chaining fashion at both layers. This way, it resolves the poor performance of the first layer and also incorporates label correlation at the second layer. The StaC method is compared with other prevalent state-of-the-art methods, including Classifier Chain and Stacking, using ten benchmark multi-label datasets. Experimental results show that the proposed StaC method achieves better performance than other state-of-the-art methods for different performance evaluation parameters.
Keywords:
Multi-label learning
Label dependence
Stacking
chaining
Stacked chaining
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7.5
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