Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Hierarchical multi-label prediction of gene function
Bioinformatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Deep classification in large-scale text hierarchies
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Decision trees for hierarchical multi-label classification
Machine Learning
Multi-label Hierarchical Classification of Protein Functions with Artificial Immune Systems
BSB '08 Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
True Path Rule Hierarchical Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Learning and evaluation in the presence of class hierarchies: application to text categorization
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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Several classification tasks in different application domains can be seen as hierarchical classification problems. In order to deal with hierarchical classification problems, the use of existing flat classification approaches is not appropriate. For these reason, there has been a growing number of studies focusing on the development of novel algorithms able to induce classification models for hierarchical classification problems. In this paper we study the performance of a novel algorithm called Hierarchical Classification using a Competitive Neural Network (HC-CNN) and compare its performance against the Global-Model Naive Bayes (GMNB) on eight protein function prediction datasets. Interestingly enough, the comparison of two global-model hierarchical classification algorithms for single path of labels hierarchical classification problems has never been done before.