Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Ant Colony Optimization
Survey of Improving Naive Bayes for Classification
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Top-Down Hierarchical Ensembles of Classifiers for Predicting G-Protein-Coupled-Receptor Functions
BSB '08 Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Using a local discovery ant algorithm for Bayesian network structure learning
IEEE Transactions on Evolutionary Computation
Bioinformatics
International Journal of Data Mining and Bioinformatics
Editorial survey: swarm intelligence for data mining
Machine Learning
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
ABC-miner: an ant-based bayesian classification algorithm
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
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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 ageing-related protein dataset created for this research.