Decision functions for chain classifiers based on Bayesian networks for multi-label classification

Varando, Gherardo; Bielza, Concha; Larrañaga, Pedro

doi:10.1016/j.ijar.2015.06.006

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…This is not the only representation a BN classifier can have as a MDPM [see e.g. 33,34]. Since BN classifiers are MMs, we can apply our methodology and deduce the following result.…”

Section: Bn Classifiersmentioning

confidence: 96%

A geometric characterisation of sensitivity analysis in monomial models

Leonelli,

Riccomagno

2019

Preprint

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Sensitivity analysis in probabilistic discrete graphical models is usually conducted by varying one probability value at a time and observing how this affects output probabilities of interest. When one probability is varied then others are proportionally covaried to respect the sum-to-one condition of probability laws. The choice of proportional covariation is justified by a variety of optimality conditions, under which the original and the varied distributions are as close as possible under different measures of closeness. For variations of more than one parameter at a time proportional covariation is justified only in some special cases only. In this work, for the large class of discrete statistical models entertaining a regular monomial parametrisation, we demonstrate the optimality of newly defined proportional multiway schemes with respect to an optimality criterion based on the notion of I-divergence. We demonstrate that there are varying parameters choices for which proportional covariation is not optimal and identify the sub-family of model distributions where the distance between the original distribution and the one where probabilities are covaried proportionally is minimum. This is shown by adopting a new formal, geometric characterization of sensitivity analysis in monomial models, which include a wide array of probabilistic graphical models. We also demonstrate the optimality of proportional covariation for multi-way analyses in Naive Bayes classifiers.

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“…This is not the only representation a BN classifier can have as a MDPM [see e.g. 33,34]. Since BN classifiers are MMs, we can apply our methodology and deduce the following result.…”

Section: Bn Classifiersmentioning

confidence: 96%

A geometric characterisation of sensitivity analysis in monomial models

Leonelli,

Riccomagno

2019

Preprint

View full text Add to dashboard Cite

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“…Some of the existing literature [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] adopted Graph Representation to express label couplings and rank labels simultaneously. Sucar et al [ 25 ] introduced a method of chaining Bayesian classifiers that integrates the advantages of CC and Bayesian networks (BN) to address the MLC problem.…”

Section: Preliminariesmentioning

confidence: 99%

“…They discovered that highly correlated labels can be sequentially ordered in chains obtained from the DAG. Varando et al [ 29 ] studied the decision boundary of the CC method when Bayesian network-augmented naïve Bayes classifiers were used as base models. It found polynomial expressions for the multi-valued decision functions and proved that the CC algorithm provided a more expressive model than the binary relevance (BR) method.…”

Section: Preliminariesmentioning

confidence: 99%

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

Wang

Wan

et al. 2020

Entropy

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Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.

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“…By imposing a BN constraint on the random order, an improved CC approach with tree-based structure was proposed [47]. Furthermore, BN-augmented naive Bayes classifiers are used as the base models for CC approach [54]. However, to the best of our knowledge, using BN model for comprehensive label correlation analysis has not been investigated yet, which will be the main focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain

Wang

2019

Preprint

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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). First, conditional entropy is used to describe the dependency relations among labels. Then, 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. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed method.

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Decision functions for chain classifiers based on Bayesian networks for multi-label classification

Cited by 13 publications

References 15 publications

A geometric characterisation of sensitivity analysis in monomial models

A geometric characterisation of sensitivity analysis in monomial models

Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain

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