A Three-phase Augmented Classifiers Chain Approach Based on
Co-occurrence Analysis for Multi-Label Classification
- URL: http://arxiv.org/abs/2204.06138v1
- Date: Wed, 13 Apr 2022 02:10:14 GMT
- Title: A Three-phase Augmented Classifiers Chain Approach Based on
Co-occurrence Analysis for Multi-Label Classification
- Authors: Gao Pengfei, Lai Dedi, Zhao Lijiao, Liang Yue, Ma Yinglong
- Abstract summary: existing Chains methods are difficult to model and exploit the underlying dependency in the label space.
We present a three-phase augmented Chain approach based on co-occurrence analysis for multilabel classification.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As a very popular multi-label classification method, Classifiers Chain has
recently been widely applied to many multi-label classification tasks. However,
existing Classifier Chains methods are difficult to model and exploit the
underlying dependency in the label space, and often suffer from the problems of
poorly ordered chain and error propagation. In this paper, we present a
three-phase augmented Classifier Chains approach based on co-occurrence
analysis for multi-label classification. First, we propose a co-occurrence
matrix method to model the underlying correlations between a label and its
precedents and further determine the head labels of a chain. Second, we propose
two augmented strategies of optimizing the order of labels of a chain to
approximate the underlying label correlations in label space, including Greedy
Order Classifier Chain and Trigram Order Classifier Chain. Extensive
experiments were made over six benchmark datasets, and the experimental results
show that the proposed augmented CC approaches can significantly improve the
performance of multi-label classification in comparison with CC and its popular
variants of Classifier Chains, in particular maintaining lower computational
costs while achieving superior performance.
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