An evaluation of global-model hierarchical classification algorithms for hierarchical classification problems with single path of labels

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.

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