Machine Learning
Machine Learning
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Boosting multi-label hierarchical text categorization
Information Retrieval
Decision trees for hierarchical multi-label classification
Machine Learning
Exploiting known taxonomies in learning overlapping concepts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ImageCLEF 2009 medical image annotation task: PCTs for hierarchical multi-label classification
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Multi-label classification and extracting predicted class hierarchies
Pattern Recognition
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Hierarchical annotation of medical images
Pattern Recognition
Hierarchical multilabel protein function prediction using local neural networks
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
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
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In Hierarchical Multi-Label Classification (HMC) problems, each example can be classified into two or more classes simultaneously, differently from standard classification. Moreover, the classes are structured in a hierarchy, in the form of either a tree or a directed acyclic graph. Therefore, an example can be assigned to two or more paths from a hierarchical structure, resulting in a complex classification problem with possibly hundreds or thousands of classes. Several methods have been proposed to deal with such problems, some of them employing a single classifier to deal with all classes simultaneously (global methods), and others employing many classifiers to decompose the original problem into a set of subproblems (local methods). In this work, we propose a novel global method called HMC-GA, which employs a genetic algorithm for solving the HMC problem. In our approach, the genetic algorithm evolves the antecedents of classification rules, in order to optimize the level of coverage of each antecedent. Then, the set of optimized antecedents is selected to build the corresponding consequent of the rules (set of classes to be predicted). Our method is compared to state-of-the-art HMC algorithms, in protein function prediction datasets. The experimental results show that our approach presents competitive predictive accuracy, suggesting that genetic algorithms constitute a promising alternative to deal with hierarchical multi-label classification of biological data.