A Global-Model Naive Bayes Approach to the Hierarchical Prediction of Protein Functions

A new global--model approach for hierarchical classification, where a single global classification model is built by considering all the classes in the hierarchy -- rather than building a number of local classification models as it is more usual in hierarchical classification.

Abstract

In this paper we propose a new global-model approach for hierarchical classification, where a single global classification model is built by considering all the classes in the hierarchy - rather than building a number of local classification models as it is more usual in hierarchical classification. The method is an extension of the flat classification algorithm naive Bayes. We present the extension made to the original algorithm as well as its evaluation on eight protein function hierarchical classification datasets. The achieved results are positive and show that the proposed global model is better than using a local model approach.