Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions

Abstract

Hierarchical Multi-Label Classification is a complex classification problem where the classes are hierarchically structured. This task is very common in protein function prediction, where each protein can have more than one function, which in turn can have more than one sub-function. In this paper, we propose a novel hierarchical multi-label classification algorithm for protein function prediction, namely HMC-PC. It is based on probabilistic clustering, and it makes use of cluster membership probabilities in order to generate the predicted class vector. We perform an extensive empirical analysis in which we compare our new approach to four different hierarchical multi-label classification algorithms, in protein function datasets structured both as trees and directed acyclic graphs. We show that HMC-PC achieves superior or comparable results compared to the state-of-the-art method for hierarchical multi-label classification.

Cite

Text

Barros et al. "Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40991-2_25

Markdown

[Barros et al. "Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/barros2013ecmlpkdd-probabilistic/) doi:10.1007/978-3-642-40991-2_25

BibTeX

@inproceedings{barros2013ecmlpkdd-probabilistic,
  title     = {{Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions}},
  author    = {Barros, Rodrigo C. and Cerri, Ricardo and Freitas, Alex Alves and de Leon Ferreira de Carvalho, André Carlos Ponce},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2013},
  pages     = {385-400},
  doi       = {10.1007/978-3-642-40991-2_25},
  url       = {https://mlanthology.org/ecmlpkdd/2013/barros2013ecmlpkdd-probabilistic/}
}