Using a mixture of probabilistic decision trees for direct prediction of protein function
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Optimizing amino acid groupings for GPCR classification
Bioinformatics
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
A Global-Model Naive Bayes Approach to the Hierarchical Prediction of Protein Functions
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
International Journal of Data Mining and Bioinformatics
Improving the performance of hierarchical classification with swarm intelligence
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
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Automatically inferring the function of unknown proteins is a challenging task in proteomics. There are two major problems in the task of computational protein function prediction, which are the choice of the protein representation and the choice of the classification algorithm. There are several ways of extracting features from a protein, and the choice of the feature representation might be as important as the choice of the classification algorithm. These problems are aggravated in the case of hierarchical protein function prediction, where a hierarchy of classifiers is built and each of those classifiers' construction has to consider the aforementioned selection problems. In this paper we address these problem by employing three alternative selective hierarchical classification approaches: a selecting the best classifier given a fixed representation; b selecting the best representation given a fixed classifier; and c selecting the best classifier and representation simultaneously, in a synergistic fashion. The analysis of the results have shown that the selective representation approach is almost always ranked number 1 when compared against the different fixed representations and that the use of the selective classifier approach is not able to surpass using only the best classifier for the target problem.