Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
C4.5: programs for machine learning
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The nature of statistical learning theory
The nature of statistical learning theory
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Simultaneous Prediction of Mulriple Chemical Parameters of River Water Quality with TILDE
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Hierarchical multi-label prediction of gene function
Bioinformatics
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Decision trees for hierarchical multi-label classification
Machine Learning
Improved Multilabel Classification with Neural Networks
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
True Path Rule Hierarchical Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Exploiting known taxonomies in learning overlapping concepts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Decision trees for hierarchical multilabel classification: a case study in functional genomics
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A genetic algorithm for Hierarchical Multi-Label Classification
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Hierarchical multi-label classification using local neural networks
Journal of Computer and System Sciences
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Protein function predictions are usually treated as classification problems where each function is regarded as a class label. However, different from conventional classification problems, they have some specificities that make the classification task more complex. First, the problem classes (protein functions) are usually hierarchically structured, with superclasses and subclasses. Second, proteins can be simultaneously assigned to more than one class in each hierarchical level, i.e., a protein can be classified into two or more paths of the hierarchical structure. This classification task is named hierarchical multilabel classification, and several methods have been proposed to deal with it. These methods, however, either transform the original problem into a set of simpler problems, loosing important information in this process, or employ complex internal mechanisms. Additionally, current methods have problems dealing with a high number of classes and also when predicting classes located in the deeper hierarchical levels, because the datasets become very sparse as their hierarchies are traversed toward the leaves. This paper investigates the use of local artificial neural networks for hierarchical multilabel classification, particularly protein function prediction. The proposed method was compared with state-of-the-art methods using several protein function prediction datasets. The experimental results suggest that artificial neural networks constitute a promising alternative to deal with hierarchical multilabel classification problems.