scholarly journals Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons

2014 ◽  
Vol 13 (2) ◽  
pp. 193-208 ◽  
Author(s):  
Bojan Mihaljević ◽  
Ruth Benavides-Piccione ◽  
Concha Bielza ◽  
Javier DeFelipe ◽  
Pedro Larrañaga
Keyword(s):  
Bayesian Network ◽  
Gabaergic Interneurons ◽  
Bayesian Network Classifiers

scholarly journals Learning Bayesian Network Classifiers: Searching in a Space of Partially Directed Acyclic Graphs

2005 ◽  
Vol 59 (3) ◽  
pp. 213-235 ◽  
Author(s):  
Silvia Acid ◽  
Luis M. de Campos ◽  
Javier G. Castellano
Keyword(s):  
Bayesian Network ◽  
Directed Acyclic Graphs ◽  
Bayesian Network Classifiers ◽  
Acyclic Graphs

scholarly journals A Symbolic Approach to Explaining Bayesian Network Classifiers

2018 ◽  
Author(s):  
Andy Shih ◽  
Arthur Choi ◽  
Adnan Darwiche
Keyword(s):  
Bayesian Network ◽  
Case Studies ◽  
Decision Diagrams ◽  
Minimal Set ◽  
Symbolic Form ◽  
Bayesian Network Classifiers ◽  
Current State ◽  
Current Classification ◽  
Decision Functions ◽  
Symbolic Approach

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.

Learning Attentive Fusion of Multiple Bayesian Network Classifiers

2012 ◽  
pp. 133-140
Author(s):  
Sepehr Eghbali ◽  
Majid Nili Ahmadabadi ◽  
Babak Nadjar Araabi ◽  
Maryam Mirian
Keyword(s):  
Bayesian Network ◽  
Bayesian Network Classifiers

Supervised Classification with Bayesian Networks

2012 ◽  
pp. 72-102 ◽  
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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