An Efficient Algorithm for Hierarchical Classification of Protein and Gene Functions

Abstract

The classification of protein and gene functions is a complex problem that is becoming more relevant as the number of sequenced genes and proteins increases. This work presents a modified version of the Extended Local Hierarchical Naive Bayes algorithm, which exploits the requirements of the original algorithm (single-path, mandatory-leaf-prediction hierarchical classification problems in tree-structured class hierarchies) to greatly improve classification run-time. We show that, considering 18 hierarchical classification datasets, the modified algorithm yields equivalent predictive performance and significantly improves run-time in the training and prediction phases.