General and Local: Averaged k-Dependence Bayesian Classifiers

Wang

2017

Entropy

Self Cite

Bayesian network classifiers (BNCs) have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN) classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN) by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.

“…Dependence relations between attributes may vary from one testing instance to another [5]. The conventional KDB structure can not automatically adapt to different testing instances.…”

Section: Tan and Watanmentioning

confidence: 99%

Section: Tan and Watanmentioning

confidence: 99%

Section: Tan and Watanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

Wang

2017

Entropy

Self Cite

Robust Structure Learning of Bayesian Network by Identifying Significant Dependencies

et al. 2019

Self Cite

Bayesian networks have long been a popular medium for graphically representing the probabilistic dependencies which exist in a domain. State-of-the-art tree-augmented naive Bayes (TAN) builds maximum weighted spanning tree to represent 1-dependence relationships between attributes. In this paper, we propose to optimize the structure of TAN applying heuristic search to sort attribute and filtering technique to remove weak conditional dependencies. Extensive experimental results on 35 data sets from University of California at Irvine (UCI) machine learning repository reveal that the proposed algorithm achieves competitive generalization performance and even outperforms higher-dependence BNCs like kdependence Bayesian network while retaining excellent strucutre complexity. INDEX TERMS Bayesian network, heuristic search, conditional dependence filtering.

Scalable Structure Learning of K-Dependence Bayesian Network Classifier

Ren

Wang

2020

IEEE Access

K-dependence Bayesian network classifier(KDB) has been widely used in data mining and machine learning. To enhance the expression ability and classification performance of KDB, the present study proposes a scalable k-dependence Bayesian classifier (SKDB), which is an extension of the classic KDB algorithm. SKDB introduces a novel attribute sorting method to pre-determine the optimal attribute order and a filtering mechanism to eliminate weak conditional dependence. Experimental results on 30 datasets from the University of California at Irvine (UCI) machine learning repository demonstrate that the above two techniques exert a positive impact on the classification performance of KDB. The proposed algorithm(SKDB) achieves better classification performance compared with several state-of-the-art BNCs (such as Naïve Bayes, tree-augmented Naïve Bayes, averaged one-dependence estimators, and KDB) in terms of 0-1 loss, bias, and variance.INDEX TERMS Bayesian network, attribute sorting, filtering mechanism.