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Prüfer Number Encoding for Genetic Bayesian Network Structure Learning Algorithm
2008 Edition, September 1, 2008 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Bayesian networks encode causal relations between variables using probability and graph theory. We employ genetic algorithm to exploit these causal relations from data for classification problems, thus restricting the search space from directed acyclic...

An Island Model Genetic Algorithm for Bayesian network structure learning
2012 Edition, June 1, 2012 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Bayesian Networks (BNs) are graphical probabilistic models that represent relationships that may exist between variables of a dataset. BN can be applied to data in a variety of different ways. Yet, using a BN requires knowing its structure. BN structure learning...

Learning bayesian network by genetic algorithm using structure-parameter restrictions
2013 Edition, July 1, 2013 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

In this paper, a novel Bayesian Network (BN) learning method is proposed, in which Genetic Algorithm (GA)and structure-parameter restrictions are combined to optimize the BN's structure and parameters simultaneously. We firstlytransferred the domain...

Genetic CNN
2017 Edition, October 1, 2017 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

The deep convolutional neural network (CNN) is the state-of-the-art solution for large-scale visual recognition. Following some basic principles such as increasing network depth and constructing highway connections, researchers have manually designed a lot of fixed...

Structure learning of Bayesian networks using a semantic genetic algorithm-based approach
2005 Edition, January 1, 2005 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

A Bayesian network model is a popular technique for data mining due to its intuitive interpretation. This paper presents a semantic genetic algorithm (SGA) to learn a complete qualitative structure of a Bayesian network from a database. SGA...

Bayesian network structure learning based on cuckoo search algorithm
2018 Edition, February 1, 2018 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

Bayesian network is a graphical model based on probabilities to represent and inference in uncertain conditions. In the field of Bayesian network, structure learning from data is an important challenge. One of the methods to learn structure of a...

Learning Bayesian Network Structures with Discrete Particle Swarm Optimization Algorithm
2007 Edition, April 1, 2007 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

A novel structure learning algorithm of Bayesian networks (BNs) using particle swarm optimization (PSO) is proposed. For searching in structure spaces efficiently, a discrete PSO algorithm is designed in term of the characteristics of BNs....

Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters
1996 Edition, Volume 18, September 1, 1996 - IEEE - Institute of Electrical and Electronics Engineers, Inc.

We present a new approach to structure learning in the field of Bayesian networks. We tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy...

Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step...

Learning to Extract Action Descriptions from Narrative Text
Volume PP - IEEE - Institute of Electrical and Electronics Engineers, Inc.

This paper focuses on the mapping of natural language sentences in written stories to a structured knowledge representation. This process yields an exponential explosion of instance combinations since each sentence may contain a set of ambiguous terms, each one giving place to a set of...

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