Ant colony optimization for learning Bayesian networks

Campos, Luis M. de; Fernández‐Luna, Juan M.; Gámez, José A.; Puerta, José M.

doi:10.1016/s0888-613x(02)00091-9

Cited by 159 publications

(77 citation statements)

References 26 publications

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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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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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“…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%

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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“…There are many BN learning algorithms that perform a search more powerful than local search but use the same basic operators, as variable neighborhood search [10], tabu search [2] or GRASP 4 [9], or even a subset of them (arc insertion), as ant colony optimization [8]. These algorithms can be used together with the restrictions with almost no additional modification.…”

Section: Is Consistent With the Restrictions If And Only Ifmentioning

confidence: 99%

On the Use of Restrictions for Learning Bayesian Networks

Campos

Castellano

2005

Lecture Notes in Computer Science

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Abstract. In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian network representing this domain should satisfy them. Our objective is to study whether the algorithms for automatically learning Bayesian networks from data can benefit from this prior knowledge to get better results. We formally define three types of restrictions: existence of arcs and/or edges, absence of arcs and/or edges, and ordering restrictions, and also study their interactions and how they can be managed within Bayesian network learning algorithms based on the score+search paradigm. Then we particularize our study to the classical local search algorithm with the operators of arc addition, arc removal and arc reversal, and carry out experiments using this algorithm on several data sets.

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Ant colony optimization for learning Bayesian networks

Cited by 159 publications

References 26 publications

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

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

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

On the Use of Restrictions for Learning Bayesian Networks

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