Learning Bayesian Belief Network Classifiers: Algorithms and System

By Jie Cheng, R. Greiner. Canadian Conference on AI, 2001

332 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6430bcfc57e7ff285e55fb9dd5b7a2c5a2451be8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Constantin F. Aliferis
2. Alexander R. Statnikov
3. Ioannis Tsamardinos
4. Subramani Mani
5. Xenofon D. Koutsoukos
6. Junfeng Yang
7. Yinzhi Cao
8. Yannis Manolopoulos

In Information Systems

1. Ioannis Tsamardinos
2. Junfeng Yang
3. S. Kotsiantis
4. Alexander R. Statnikov
5. P. Pintelas
6. Yannis Manolopoulos
7. Constantin F. Aliferis
8. Charalambos Spathis

In Management Science and Operations Research

1. Constantin F. Aliferis
2. Ioannis Tsamardinos
3. Alexander R. Statnikov
4. Yannis Manolopoulos
5. Efstathios Kirkos
6. Subramani Mani
7. Junfeng Yang
8. S. Kotsiantis

Citations in the corpus

• Supervised Machine Learning: A Review of Classification Techniques (2007, positive): “This suggests that simple BN classifiers should work better when there is a single underlying model of the dataset and multi-net classifier should work better when the underlying relationships among the features are very different for different classes (Cheng and Greiner, 2001).”
• Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning (2018, background): “The general problems of overfitting and underfitting are orthogonal to the paper, and one may refer to the literature [16, 40] for the problem.”
• Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “…and Näıve-Bayes graphs (Duda and Hart, 1973), while modern versions are exemplified in (i) TAN/BAN classifiers that relax the Naı̈ve-Bayes structure (Cheng and Greiner, 2001, 1999; Friedman et al., 1997), (ii) efficient complete model averaging of Naı̈ve-Bayes classifiers (Dash and Cooper,…”
• Data Mining techniques for the detection of fraudulent financial statements (2007, methodology): “The implemented algorithm belongs to the category of conditional independence test-based algorithms and does not require node ordering (Cheng & Greiner, 2001).”
• Machine learning: a review of classification and combining techniques (2006, background): “This suggests that simple BN classifiers should work better when there is a single underlying model of the dataset and multi-net classifier should work better when the underlying relationships among the features are very different for different classes (Cheng and Greiner 2001).”