A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier Article Swipe
Guiliang Ou
,
Yulin He
,
Philippe Fournier‐Viger
,
Joshua Zhexue Huang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122010443
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app122010443
The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC).
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app122010443
- https://www.mdpi.com/2076-3417/12/20/10443/pdf?version=1665991318
- OA Status
- gold
- Cited By
- 14
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306392307
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306392307Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app122010443Digital Object Identifier
- Title
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A Novel Mixed-Attribute Fusion-Based Naive Bayesian ClassifierWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-17Full publication date if available
- Authors
-
Guiliang Ou, Yulin He, Philippe Fournier‐Viger, Joshua Zhexue HuangList of authors in order
- Landing page
-
https://doi.org/10.3390/app122010443Publisher landing page
- PDF URL
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https://www.mdpi.com/2076-3417/12/20/10443/pdf?version=1665991318Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/12/20/10443/pdf?version=1665991318Direct OA link when available
- Concepts
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Naive Bayes classifier, Artificial intelligence, Computer science, Weighting, Machine learning, Pattern recognition (psychology), Artificial neural network, Data mining, Classifier (UML), Support vector machine, Medicine, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 6, 2023: 5Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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