Book Chapter10.1007/978-3-540-89985-3_4
A bayesian network based approach for data classification using structural learning
Alireza Khanteymoori,Mohammad Mehdi Homayounpour,Mohammad Bagher Menhaj +2 more
- 09 Mar 2008
- Vol. 6, pp 25-32
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TL;DR: The simulation results approved that using structural learning in order to find Bayesian networks structure improves the classification accuracy and it was shown that the Iterative Hill Climbing is the most appropriate search algorithm and K2 is the simplest one with the least time complexity.
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Abstract: This paper describes the theory and implementation of Bayesian networks in the context of data classification Bayesian networks provide a very general and yet effective graphical language for factoring joint probability distributions which in turn make them very popular for classification Finding the optimal structure of Bayesian networks from data has been shown to be NP-hard In this paper score-based algorithms such as K2, Hill Climbing, Iterative Hill Climbing and simulated annealing have been developed to provide more efficient structure learning through more investigation on MDL, BIC and AIC scores borrowed from information theory Our experimental results show that the BIC score is the best one though it is very time consuming Bayesian naive classifier is the simplest Bayesian network with known structure for data classification For the purpose of comparison, we considered several cases and applied general Bayesian networks along with this classifier to these cases The simulation results approved that using structural learning in order to find Bayesian networks structure improves the classification accuracy Indeed it was shown that the Iterative Hill Climbing is the most appropriate search algorithm and K2 is the simplest one with the least time complexity
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Citations
Classification and speed estimation of vehicles via tire detection using single-element piezoelectric sensor
TL;DR: Novel vehicle classification technology is presented by utilizing a single-element piezoelectric sensor placed diagonally on a traffic lane to accurately identify vehicles to provide a highly accurate and cost-effective alternative to current vehicle classification systems.
29
Structure Learning of Bayesian Networks Using Heuristic Methods
Alireza Sadeghi Hesar,Hamid Tabatabaee,Mehrdad Jalali,Ghoochan Branch +3 more
- 01 Jan 2012
TL;DR: The main purpose of this paper is to determine the algorithm which produces the Bayesian network with the highest predictive accuracy, and is constructed in the least amount of time.
Structure Learning of Bayesian Belief Networks Using Simulated Annealing Algorithm
Alireza Sadeghi Hesar,Mashhad Branch +1 more
- 01 Jan 2013
TL;DR: Experimental results of research show that the simulated annealing algorithm is the best structure learning algorithm from the point of construction time but needs to more attention for classification process.
Speaker identification in noisy environments using dynamic Bayesian networks
Alireza Khanteymoori,Mohammad Mehdi Homayounpour,Mohammad Bagher Menhaj +2 more
- 08 Dec 2009
TL;DR: Dynamic Bayesian networks provide a succinct and expressive graphical language for factoring joint probability distributions, and this approach is notable because it expresses an identification system using only the concepts of random variables and conditional probabilities.
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Developing a pieces of data allocation method in distributed databases using Bayesian networks
Soheil Pourhaji,Mohammad Hossein Moattar +1 more
- 01 Nov 2015
TL;DR: This model is compared with the results of Huang and Chen's method which have used a heuristic approach using the imperialist competitive algorithm and indicates that this model is an efficient tool in the optimization process of allocating Pieces of Data in the distributed systems.
6
References
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Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
- 01 Jan 2020
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
21.4K
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl
- 01 Jan 1988
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
17.6K
Bayesian Network Classifiers
TL;DR: Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
TL;DR: In this article, a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data is presented, which is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence.
Bayesian Network Classifiers
Moises Goldszmidt
- 14 Jan 2011
TL;DR: The main concepts behind statistical pattern classifiers and Bayesian networks, including the main methods for the automated induction of these models are reviewed, and the advantages of Bayesian network classifiers over other types of classifiers are discussed.
3.1K