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Neural Computing and Applications

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05-03-2018 | Original Article

Decision function with probability feature weighting based on Bayesian network for multi-label classification

Authors: Youlong Yang, Mengxiao Ding

Published in: Neural Computing and Applications | Issue 9/2019

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Abstract

The multi-label classification problem involves finding a multi-valued decision function that predicts an instance to a vector of binary classes. Two methods are widely used to build multi-label classifiers: the binary relevance method and the chain classifier. Both can induce a polynomial multi-valued decision function by using Bayesian network-augmented naive Bayes classifiers as base models. In this paper, we propose a feature weighting approach to improve the classification accuracy of the decision function. This method, called probability feature weighting, estimates the conditional probability of the positive class through deep computation of the frequency ratio of features from the training data. Moreover, we identify irrelevant variables in terms of probability to simplify the decision function. Experiments showed that the decision function with a probability feature weighting rarely degrades the quality of the model and drastically improves it in many cases.

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Title: Decision function with probability feature weighting based on Bayesian network for multi-label classification
Authors: Youlong Yang
Mengxiao Ding
Publication date: 05-03-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 9/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-3323-y

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