A Bayesian network correlation-based classifier chain algorithm for multilabel learning
by Hao Zhang; Kai-Biao Lin; Wei Weng; Juan Wen; Chin-Ling Chen
International Journal of Computational Science and Engineering (IJCSE), Vol. 25, No. 4, 2022

Abstract: This article introduces a new multilabel learning method, the Bayesian network correlation-based CC (BNCC) algorithm, to decrease the uncertainty in the label order from the classifier chain (CC) algorithm. It uses a neural network constructed in TensorFlow as the classifier of all labels and calculates the corresponding error function, which is used to eliminate the influence of the feature set on all labels. A directed acyclic graph (DAG) Bayesian network is constructed by using the error function to identify the correlations between the labels. The optimal correlation label is identified via topological sorting. Finally, the sorted sequence is used as the chain order of the CC. The experimental results demonstrate that the proposed method is superior to the unordered CC model and other multilabel learning algorithms on several benchmark datasets.

Online publication date: Thu, 28-Jul-2022

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