Multi-view multi-class classification for identification of pathogenic bacterial strains

Multi-view multi-class classification for identification of pathogenic bacterial strains

Tsivtsivadze, E.
Heskes, T.
Paauw, A.

2013

In various learning problems data can be available in different representations, often referred to as views. We propose multi-class classification method that is particularly suitable for multi-view learning setting. The algorithm uses co-regularization and error-correcting techniques to leverage information from multiple views and in our empirical evaluation notably outperforms several state-of-the-art classification methods on publicly available datasets. Furthermore,we apply the proposed algorithm for identification of the pathogenic bacterial strains from the recently collected biomedical dataset. Our algorithm gives a low classification error rate of 5%, allows rapid identification of the pathogenic microorganisms, and can aid effective response to an infectious disease outbreak. © Springer-Verlag 2013.

Life
MSB - Microbiology and Systems Biology
EELS - Earth, Environmental and Life Sciences
Biomedical Innovation
Biology
Healthy Living

http://resolver.tudelft.nl/uuid:46df4bd9-887c-4958-84e3-ced40eb10f96

https://doi.org/10.1007/978-3-642-38067-9_6

488283

9783642380662

0302-9743

11th International Workshop on Multiple Classifier Systems, MCS 2013, 15 May 2013 through 17 May 2013, Nanjing, 7872 LNCS, 61-72

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

conference paper