Learning Bayesian network classifiers using ant colony optimization

Salama, Khalid M.; Freitas, Alex A.

doi:10.1007/s11721-013-0087-6

Cited by 31 publications

(18 citation statements)

References 23 publications

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“…K.Salama and A. A. Freitas [29], exploited ant colony optimization for the learning Bayesian network classifiers which resulted in promising results. On the basis of importance and effectiveness of knowledge discovery from huge data reservoirs, discussions were provided by the A.A Freitas in [30].…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Ali¹,

Shahzad²

2017

ijacsa

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Ali¹,

Shahzad²

2017

ijacsa

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“…These methods include work like learning Bayesian network structures using Ant Colony Optimization [17] and abductive inference using PSO [8]. However, to our knowledge, no work has been published using PSO or multi-population methods for parameter estimation.…”

Section: Related Workmentioning

confidence: 99%

Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence

Fortier

Sheppard

Strasser

2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Bayesian networks are probabilistic graphical models that have proven to be able to handle uncertainty in many realworld applications. One key issue in learning Bayesian networks is parameter estimation, i.e., learning the local conditional distributions of each variable in the model. While parameter estimation can be performed efficiently when complete training data is available (i.e., when all variables have been observed), learning the local distributions becomes difficult when latent (hidden) variables are introduced. While Expectation Maximization (EM) is commonly used to perform parameter estimation in the context of latent variables, EM is a local optimization method that often converges to sub-optimal estimates. Although several authors have improved upon traditional EM, few have applied population based search techniques to parameter estimation, and most existing population-based approaches fail to exploit the conditional independence properties of the networks. We introduce two new methods for parameter estimation in Bayesian networks based on particle swarm optimization (PSO). The first is a single swarm PSO, while the second is a multiswarm PSO algorithm. In the multi-swarm version, a swarm is assigned to the Markov blanket of each variable to be estimated, and competition is held between overlapping swarms. Results of comparing these new methods to several existing approaches indicate that the multi-swarm algorithm outperforms the competing approaches when compared using data generated from a variety of Bayesian networks.

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“…A er the introduction of Ant-Miner, research on ACO classi cation algorithms a racted greater a ention-the original Ant-Miner paper has more than 990 citations according to Google Scholar-and a large number of variations have been proposed in the literature [14]. While the vast majority ACO algorithms for classi cation are focused on creating classi cation rules, there are also works focused on creating decision trees [2,22], hierarchical classi cation [18,21], learning bayesian network classi ers [25] and training arti cial neural networks [1]. Despite the large number of publications proposing ACO classi cation algorithms, the algorithms' implementations are not publicly released in most cases.…”

Section: Introductionmentioning

confidence: 99%

Myra

Otero

2017

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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is paper introduces MYRA, an open-source Java framework that provides the implementation of several ant colony optimization classi cation algorithms. e algorithms are ready to be used from the command-line or can be easily called from custom Java code. e framework is implemented using a modular architecture, therefore algorithms can be easily extended to incorporate di erent procedures and/or use di erent parameter values. e paper gives particular a ention to the common architecture from which the algorithms are built on, highlighting the shared classes among the di erent implemented algorithms. e source code and documentation of MYRA are available for download at h ps://github.com/febo/myra.

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Learning Bayesian network classifiers using ant colony optimization

Cited by 31 publications

References 23 publications

Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners

Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence

Myra

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