Bayesian network based label correlation analysis for multi-label classifier chain

Ran WANG, Suhe YE, Ke LI, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

26 Citations (Scopus)

Abstract

Classifier chain (CC) is a multi-label learning approach that constructs a sequence of binary classifiers according to a label order. Each classifier in the sequence is responsible for predicting the relevance of one label. When training the classifier for a label, proceeding labels will be taken as extended features. If the extended features are highly correlated to the label, the performance will be improved, otherwise, the performance will not be influenced or even degraded. How to discover label correlation and determine the label order is critical for CC approach. This paper employs Bayesian network (BN) to model the label correlations and proposes a new BN-based CC method (BNCC). Conditional entropy is used to describe the dependency relations among labels, and a BN is built up by taking nodes as labels and weights of edges as their dependency relations. A new scoring function is proposed to evaluate a BN structure, and a heuristic algorithm is introduced to optimize the BN. At last, by applying topological sorting on the nodes of the optimized BN, the label order for constructing CC model is derived. Experiments demonstrate the feasibility and effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)256-275
JournalInformation Sciences
Volume554
Early online date16 Dec 2020
DOIs
Publication statusPublished - Apr 2021
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China (Grant 61772344, Grant 61811530324, Grant 61732011 and Grant 62076056), in part by the HD Video R&D Platform for Intelligent Analysis and Processing in Guangdong Engineering Technology Research Centre of Colleges and Universities (Grant GCZXA1409), in part by the Natural Science Foundation of Guangdong Province of China (Grant 2020B1515310008), in part by the Natural Science Foundation of Shenzhen (Grant JCYJ20170818091621856), in part by the Interdisciplinary Innovation Team of Shenzhen University, and in part by UKRI Future Leaders Fellowship (Grant No. MR/S017062/1) and Royal Society (Grant No. IEC/NSFC/170243).

Keywords

  • Bayesian network
  • Classifier chain
  • Label correlation
  • Multi-label learning
  • Scoring function
  • Topological sorting

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