Using a Local Discovery Ant Algorithm for Bayesian Network Structure Learning

Pinto, Pedro Contreiras; Nägele, Andreas; Dejori, Mathäus; Runkler, Thomas A.; Sousa, J.M.

doi:10.1109/tevc.2009.2024142

Cited by 67 publications

(38 citation statements)

References 19 publications

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“…In this section, we present the experimental results carried out with our algorithm and compare MMABC with two hybrid heuristic methods: MMHC [18] and MMACO [24]. In all the Algorithm 1 The MMABC algorithm.…”

Section: Resultsmentioning

confidence: 99%

Learning Bayesian Networks in the Space of Structures by a Hybrid Optimization Algorithm

Zhu

Liu

Jiang

2016

INT J COMPUT COMMUN

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Bayesian networks (BNs) are one of the most widely used class for machine learning and decision making tasks especially in uncertain domains. However, learning BN structure from data is a typical NP-hard problem. In this paper, we present a novel hybrid algorithm for BN structure learning, called MMABC. It's based on a recently introduced meta-heuristic, which has been successfully applied to solve a variety of optimization problems: Artificial Bee Colony (ABC). MMABC algorithm consists of three phases: (i) obtain an initial undirected graph by the subroutine MMPC. (ii) Generate the initial population of solutions based on the undirected graph and (iii) perform the ABC algorithm to orient the edges. We describe all the elements necessary to tackle our learning problem, and experimentally compare the performance of our algorithm with two state-of-the-art algorithms reported in the literature. Computational results demonstrate that our algorithm achieves better performance than other two related algorithms.

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Section: Resultsmentioning

confidence: 99%

Learning Bayesian Networks in the Space of Structures by a Hybrid Optimization Algorithm

Zhu

Liu

Jiang

2016

INT J COMPUT COMMUN

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“…The main idea is to utilize a swarm of simple individuals that use collective behaviour to achieve a certain goal. ACO algorithms have been successful in solving several combinatorial optimization problems, including classification rules discovery [12,11] and general purpose BN construction [2,13,23]. However, ABC-Miner [14], recently introduced by the authors, is the first ACO algorithm to learn BN classifiers.…”

Section: The Abc-miner Algorithmmentioning

confidence: 99%

ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins

Salama

Freitas

2013

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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Abstract. The task of predicting protein functions using computational techniques is a major research area in the field of bioinformatics. Casting the task into a classification problem makes it challenging, since the classes (functions) to be predicted are hierarchically related, and a protein can have more than one function. One approach is to produce a set of local classifiers; each is responsible for discriminating between a subset of the classes in a certain level of the hierarchy. In this paper we tackle the hierarchical classification problem in a local fashion, by learning an ensemble of Bayesian network classifiers for each class in the hierarchy and combining their outputs with four alternative methods: a) selecting the best classifier, b) majority voting, c) weighted voting, and d) constructing a meta-classifier. The ensemble is built using ABC-Miner, our recently introduced Ant-based Bayesian Classification algorithm. We use different types of protein representations to learn different classification models. We empirically evaluate our proposed methods on an ageingrelated protein dataset created for this research.

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“…ACO has been employed for learning general-purpose BNs in several works [9], [10], [11], [12]. In the area of Bayesian classification, the authors have recently introduced ABCMiner [13], at present the only algorithm that uses ACO for learning a BN classifier in the structure of a BAN, rather than a Bayesian Multi-net.…”

Section: Ant Colony Optimization Backgroundmentioning

confidence: 99%

“…ACO has been successfully employed in several research areas related to our current work, classification [2], [3], [4], [5], clustering [6], [7], [8], and learning general-purpose Bayesian Networks (BNs) [9], [10], [11], [12]. Recently, the authors have introduced ABC-Miner [13], the first ACO-based algorithm to build Bayesian network classifiers, which has shown better performance compared to some greedy and deterministic BN algorithms.…”

Section: Introduction Ant Colony Optimization (Aco)mentioning

confidence: 99%

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Clustering-based Bayesian Multi-net Classifier construction with Ant Colony Optimization

Salama

Freitas

2013

2013 IEEE Congress on Evolutionary Computation

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Abstract-Bayesian Multi-nets (BMNs) are a special kind of Bayesian network (BN) classifiers that consist of several local networks, typically, one for each predictable class, to model an asymmetric set of variable dependencies given each class value. Alternatively, multi-nets can be learnt upon arbitrary partitions of a dataset, in which each partition holds more consistent variable dependencies given the data subset in the partition. This paper proposes two contributions to the approach that clusters the dataset into separate data subsets to build asymmetric local BN classifiers, one for each subset. First, we extend the K-modes algorithm, previously used by the Case-Based Bayesian Network Classifiers (CBBN) approach to create clusters before learning the BN classifiers. Second, we introduce the Ant-Clust-B algorithm that employs Ant Colony Optimization (ACO) to learn clusteringbased BMNs. Ant-Clust-B uses ACO in the clustering step before learning the local BN classifiers. Empirical results are obtained from experiments on 18 UCI datasets. [12]. Recently, the authors have introduced ABC-Miner [13], the first ACO-based algorithm to build Bayesian network classifiers, which has shown better performance compared to some greedy and deterministic BN algorithms. Thus, we carry on developing ACO-based algorithms in the Bayesian classification area. Classification is a central problem in data mining and machine learning where the system builds, from labelled data instances, a model (classifier) that predicts the class of unlabelled instances [14]. There are many types of classification methods [14], but in this work we focus on building BN classifiers. I. INTRODUCTIONA BN classifier is a special kind of probabilistic networks that aims to predict the class of a data instance by computing the posterior probability of each available class value, given the values of the predictor attributes of the instance, and then labeling the instance with the class having the highest posterior probability. Naïve-Bayes, as discussed in [15], is the simplest kind of BN classifier and it assumes the attributes are independent given the class label. Although it obtained good predictive performance in several domains [15], extensions

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Using a Local Discovery Ant Algorithm for Bayesian Network Structure Learning

Cited by 67 publications

References 19 publications

Learning Bayesian Networks in the Space of Structures by a Hybrid Optimization Algorithm

Learning Bayesian Networks in the Space of Structures by a Hybrid Optimization Algorithm

ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins

Clustering-based Bayesian Multi-net Classifier construction with Ant Colony Optimization

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