Exact Learning Augmented Naive Bayes Classifier

Sugahara, Shouta; Ueno, Maomi

doi:10.3390/e23121703

Cited by 22 publications

(29 citation statements)

References 29 publications

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“…Parts of this research were reported in an earlier conference paper published by Sugahara et al (2018).…”

Section: Acknowledgmentsmentioning

confidence: 97%

Exact Learning Augmented Naive Bayes Classifier

Sugahara,

Ueno

2021

Preprint

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Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB), which ensures a class variable with no parents. The proposed method is guaranteed to asymptotically estimate the identical class posterior to that of the exactly learned BN. Comparison experiments demonstrated the superior performance of the proposed method.

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“…Parts of this research were reported in an earlier conference paper published by Sugahara et al (2018).…”

Section: Acknowledgmentsmentioning

confidence: 97%

Exact Learning Augmented Naive Bayes Classifier

Sugahara,

Ueno

2021

Preprint

Self Cite

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“…Therefore, the model can be useful for an entity (expert, broker, or bidder) to determine relevant improvement actions to improve currently existing products. The proposed model was developed by combining these instruments: SMART(-ER) method (S-specific, M-measurable, A-achievable, R-relevant, realistic, or reward, T-'based on timeline' or timebound, E-exciting or evaluated, R-recorded or reward) [32], brainstorming method (BM) [33], survey with Likert scale [1,[34][35][36], Weighted Sum Model (WSM) [3,[37][38][39], relative states scale [3,4,40], and Naïve Bayesian Classifier (NBC) [41][42][43][44]. The general concept of the model is shown in Figure 1.…”

Section: Concept Of Modelmentioning

confidence: 99%

“…Furthermore, the Naive Bayesian Classifier (NBC) is effective in predicting or classifying any data shown in qualitative and quantitative ways [41][42][43][44]. Despite that, the universality of the model is a possibility of its application as part of the ongoing improvement of products, and to the sustainable development of products considering customers' expectations.…”

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confidence: 99%

Universal Model to Predict Expected Direction of Products Quality Improvement

2022

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Improving the quality of products remains a challenge. This is due to the turbulent environment and the dynamics of changing customer requirements. Hence, the key action is to predict beneficial changes in products, which will allow one to achieve customer satisfaction and reduce the waste of resources. Therefore, the purpose of this article was to develop a universal model to predict the expected direction of quality improvement. Initially, the purpose of the research was determined by using the SMART(-ER) method. Then, during the brainstorming method (BM), the product criteria and range states of these criteria were determined. Next, a survey with the Likert scale was used to obtain customers’ expectations, i.e., assessing the importance of criteria and customers’ satisfaction with ranges of product criteria states. Based on customer assessments, quality product levels were calculated using the Weighted Sum Model (WSM). Then, the initial customer satisfaction from the product quality level was identified according to the relative state’s scale. Based on this, the direction of product quality improvement was anticipated using the Naïve Bayesian Classifier (NBC). A test of the model was carried out for photovoltaic panels (PV) of a key EU producer. However, the proposed model is universal, because it can be used by any entity to predict the direction of improvement of any kind of product. The originality of this model allows the prediction of the destination of product improvement according to customers’ assessments for weights of criteria and satisfaction from ranges of quality-criterion states.

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“…Therefore, there are no edges between the attributes, as shown in the example in Figure 1. Improvements to the naive Bayes include feature weighting techniques [32,33], boosted parameter learning [34], and the exact learning augmented naive Bayes classifier [35]. Whereas TAN starts off by considering a fully connected network where the edges have weights, these are computed using the conditional mutual information between pairs of attributes.…”

Section: Background 21 Bayesian Networkmentioning

confidence: 99%

Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

2022

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Constitutional processes are a cornerstone of modern democracies. Whether revolutionary or institutionally organized, they establish the core values of social order and determine the institutional architecture that governs social life. Constitutional processes are themselves evolutionary practices of mutual learning in which actors, regardless of their initial political positions, continuously interact with each other, demonstrating differences and making alliances regarding different topics. In this article, we develop Tree Augmented Naive Bayes (TAN) classifiers to model the behavior of constituent agents. According to the nature of the constituent dynamics, weights are learned by the model from the data using an evolution strategy to obtain a good classification performance. For our analysis, we used the constituent agents’ communications on Twitter during the installation period of the Constitutional Convention (July–October 2021). In order to differentiate political positions (left, center, right), we applied the developed algorithm to obtain the scores of 882 ballots cast in the first stage of the convention (4 July to 29 September 2021). Then, we used k-means to identify three clusters containing right-wing, center, and left-wing positions. Experimental results obtained using the three constructed datasets showed that using alternative weight values in the TAN construction procedure, inferred by an evolution strategy, yielded improvements in the classification accuracy measured in the test sets compared to the results of the TAN constructed with conditional mutual information, as well as other Bayesian network classifier construction approaches. Additionally, our results may help us to better understand political behavior in constitutional processes and to improve the accuracy of TAN classifiers applied to social, real-world data.

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Exact Learning Augmented Naive Bayes Classifier

Cited by 22 publications

References 29 publications

Exact Learning Augmented Naive Bayes Classifier

Exact Learning Augmented Naive Bayes Classifier

Universal Model to Predict Expected Direction of Products Quality Improvement

Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

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