Compiling Bayesian Network Classifiers into Decision Graphs
نویسندگان
چکیده
منابع مشابه
Decision boundary for discrete Bayesian network classifiers
Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we a...
متن کاملCompiling Constraint Networks into Multivalued Decomposable Decision Graphs
We present and evaluate a top-down algorithm for compiling finite-domain constraint networks (CNs) into the language MDDG of multivalued decomposable decision graphs. Though it includes DecisionDNNF as a proper subset, MDDG offers the same key tractable queries and transformations as DecisionDNNF, which makes it useful for many applications. Intensive experiments showed that our compiler cn2mdd...
متن کاملAdaptive Bayesian network classifiers
Abstract This paper is concerned with adaptive learning algorithms for Bayesian network classifiers in a prequential (on-line) learning scenario. In this scenario, new data is available over time. An efficient supervised learning algorithm must be able to improve its predictive accuracy by incorporating the incoming data, while optimizing the cost of updating. However, if the process is not str...
متن کاملApproximate Bayesian Network Classifiers
Bayesian network (BN) is a directed acyclic graph encoding probabilistic independence statements between variables. BN with decision attribute as a root can be applied to classification of new cases, by synthesis of conditional probabilities propagated along the edges. We consider approximate BNs, which almost keep entropy of a decision table. They have usually less edges than classical BNs. Th...
متن کاملComparing Bayesian Network Classifiers
In this paper, we empirically evaluate algorithms for learning four Bayesian network (BN) classifiers: Naïve-Bayes, tree augmented Naïve-Bayes (TANs), BN augmented NaïveBayes (BANs) and general BNs (GBNs), where the GBNs and BANs are learned using two variants of a conditional independence based BN-learning algorithm. Experimental results show the GBNs and BANs learned using the proposing learn...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33017966