Bagging k-dependence Bayesian network classifiers
Intelligent Data Analysis, ISSN: 1571-4128, Vol: 25, Issue: 3, Page: 641-667
2021
- 5Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Bagging has attracted much attention due to its simple implementation and the popularity of bootstrapping. By learning diverse classifiers from resampled datasets and averaging the outcomes, bagging investigates the possibility of achieving substantial classification performance of the base classifier. Diversity has been recognized as a very important characteristic in bagging. This paper presents an efficient and effective bagging approach, that learns a set of independent Bayesian network classifiers (BNCs) from disjoint data subspaces. The number of bits needed to describe the data is measured in terms of log likelihood, and redundant edges are identified to optimize the topologies of the learned BNCs. Our extensive experimental evaluation on 54 publicly available datasets from the UCI machine learning repository reveals that the proposed algorithm achieves a competitive classification performance compared with state-of-the-art BNCs that use or do not use bagging procedures, such as tree-augmented naive Bayes (TAN), k-dependence Bayesian classifier (KDB), bagging NB or bagging TAN.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85105001488&origin=inward; http://dx.doi.org/10.3233/ida-205125; https://journals.sagepub.com/doi/full/10.3233/IDA-205125; https://dx.doi.org/10.3233/ida-205125; https://content.iospress.com:443/articles/intelligent-data-analysis/ida205125
SAGE Publications
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