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A Study of Hierarchical and Flat Classification of Proteins
July-September 2010 (vol. 7 no. 3)
pp. 563-571
ASCII Text
x 
 
Arthur Zimek, Fabian Buchwald, Eibe Frank, Stefan Kramer, "A Study of Hierarchical and Flat Classification of Proteins," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 3, pp. 563-571, July-September, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2008.104,
author = {Arthur Zimek and Fabian Buchwald and Eibe Frank and Stefan Kramer},
title = {A Study of Hierarchical and Flat Classification of Proteins},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {7},
number = {3},
issn = {1545-5963},
year = {2010},
pages = {563-571},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.104},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - A Study of Hierarchical and Flat Classification of Proteins
IS - 3
SN - 1545-5963
SP563
EP571
EPD - 563-571
A1 - Arthur Zimek,
A1 - Fabian Buchwald,
A1 - Eibe Frank,
A1 - Stefan Kramer,
PY - 2010
KW - Protein classification
KW - hierarchical classification
KW - multiclass classification.
VL - 7
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
Arthur Zimek, Ludwig-Maximilians-Universitaet Muenchen and Forschungseinheit fuer Datenbanksysteme, Muenchen
Fabian Buchwald, Technische Universitaet Muenchen, Muenchen
Eibe Frank, University of Waikato, Hamilton
Stefan Kramer, Technische Universitaet Muenchen, Muenchen
Automatic classification of proteins using machine learning is an important problem that has received significant attention in the literature. One feature of this problem is that expert-defined hierarchies of protein classes exist and can potentially be exploited to improve classification performance. In this article, we investigate empirically whether this is the case for two such hierarchies. We compare multiclass classification techniques that exploit the information in those class hierarchies and those that do not, using logistic regression, decision trees, bagged decision trees, and support vector machines as the underlying base learners. In particular, we compare hierarchical and flat variants of ensembles of nested dichotomies. The latter have been shown to deliver strong classification performance in multiclass settings. We present experimental results for synthetic, fold recognition, enzyme classification, and remote homology detection data. Our results show that exploiting the class hierarchy improves performance on the synthetic data but not in the case of the protein classification problems. Based on this, we recommend that strong flat multiclass methods be used as a baseline to establish the benefit of exploiting class hierarchies in this area.

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Index Terms:
Protein classification, hierarchical classification, multiclass classification.
Citation:
Arthur Zimek, Fabian Buchwald, Eibe Frank, Stefan Kramer, "A Study of Hierarchical and Flat Classification of Proteins," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 3, pp. 563-571, July-Sept. 2010, doi:10.1109/TCBB.2008.104
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