Randomized Bayesian Network Classifiers

We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form. We introduce two types of explanations for why a classifier may have classified an instance positively or negatively and suggest algorithms for computing these explanations. The first type of explanation identifies a minimal set of the currently active features that is responsible for the current classification, while the second type of explanation identifies a minimal set of features whose current state (active or not) is sufficient for the classification. We consider in particular the compilation of Naive and Latent-Tree Bayesian network classifiers into Ordered Decision Diagrams (ODDs), providing a context for evaluating our proposal using case studies and experiments based on classifiers from the literature.

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Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons

Neuroinformatics ◽

10.1007/s12021-014-9254-1 ◽

2014 ◽

Vol 13 (2) ◽

pp. 193-208 ◽

Cited By ~ 10

Author(s):

Bojan Mihaljević ◽

Ruth Benavides-Piccione ◽

Concha Bielza ◽

Javier DeFelipe ◽

Pedro Larrañaga

Keyword(s):

Bayesian Network ◽

Gabaergic Interneurons ◽

Bayesian Network Classifiers

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Learning Attentive Fusion of Multiple Bayesian Network Classifiers

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-34487-9_17 ◽

2012 ◽

pp. 133-140

Author(s):

Sepehr Eghbali ◽

Majid Nili Ahmadabadi ◽

Babak Nadjar Araabi ◽

Maryam Mirian

Keyword(s):

Bayesian Network ◽

Bayesian Network Classifiers

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Supervised Classification with Bayesian Networks

Intelligent Data Analysis for Real-Life Applications ◽

10.4018/978-1-4666-1806-0.ch005 ◽

2012 ◽

pp. 72-102 ◽

Cited By ~ 6

Author(s):

M. Julia Flores ◽

José A. Gámez ◽

Ana M. Martínez

Keyword(s):

Bayesian Network ◽

Dynamic Models ◽

Continuous Variables ◽

Bayesian Network Classifiers ◽

Wide Range ◽

Recent Developments ◽

Discrete Nature ◽

Good Trade ◽

And Performance ◽

Numeric Data

Bayesian Network classifiers (BNCs) are Bayesian Network (BN) models specifically tailored for classification tasks. There is a wide range of existing models that vary in complexity and efficiency. All of them have in common the ability to deal with uncertainty in a very natural way, at the same time providing a descriptive environment. In this chapter, the authors focus on the family of semi-naïve Bayesian classifiers (naïve Bayes, AODE, TAN, kDB, etc.), motivated by the good trade-off between efficiency and performance they provide. The domain of the BNs is generally of discrete nature, but since the presence of continuous variables is very common, the chapter discusses more classical and novel approaches to handling numeric data. In this chapter the authors also discuss more recent techniques such as multi-dimensional and dynamic models. Last but not least, they focus on applications and recent developments, including some of the BNCs approaches to the multi-class problem together with other traditionally successful and cutting edge cases regarding real-world applications.

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