A Bayesian network correlation-based classifier chain algorithm for multilabel learning. (28th June 2022)

• Record Type: Journal Article
• Authors: Zhang, Hao; Lin, Kai-Biao; Weng, Wei; Wen, Juan; Chen, Chin-Ling
• Is Part Of: International journal of computational science and engineering. Volume 25:Number 4(2022)
• Page Start: 437
• Page End: 447
• Publication Date: 2022-06-28
• Subjects: multilabel learning -- Bayesian network -- classifier chain -- label relationship; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.