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Optimal Aggregation of Binary Classifiers for Multiclass Cancer Diagnosis Using Gene Expression Profiles

Authors:

Naoto Yukinawa,

Shin IshiiAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 6, Issue 2

Pages 333 - 343

https://doi.org/10.1109/TCBB.2007.70239

Published: 01 April 2009 Publication History

Abstract

Multiclass classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. There have been many studies of aggregating binary classifiers to construct a multiclass classifier based on one-versus-the-rest (1R), one-versus-one (11), or other coding strategies, as well as some comparison studies between them. However, the studies found that the best coding depends on each situation. Therefore, a new problem, which we call the “optimal coding problem,” has arisen: how can we determine which coding is the optimal one in each situation? To approach this optimal coding problem, we propose a novel framework for constructing a multiclass classifier, in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. Although there is no a priori answer to the optimal coding problem, our weight tuning method can be a consistent answer to the problem. We apply this method to various classification problems including a synthesized data set and some cancer diagnosis data sets from gene expression profiling. The results demonstrate that, in most situations, our method can improve classification accuracy over simple voting heuristics and is better than or comparable to state-of-the-art multiclass predictors.

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Cited By

Takenouchi TIshii S(2018)Binary classifiers ensemble based on Bregman divergence for multi-class classificationNeurocomputing10.1016/j.neucom.2017.08.004273:C(424-434)Online publication date: 17-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.08.004
Bicego MLovato PPerina AFasoli MDelledonne MPezzotti MPolverari AMurino V(2012)Investigating Topic Models' Capabilities in Expression Microarray Data ClassificationIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2012.1219:6(1831-1836)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1109/TCBB.2012.121
Saraswathi SSundaram SSundararajan NZimmermann MNilsen-Hamilton M(2011)ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly RepresentedIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2010.138:2(452-463)Online publication date: 1-Mar-2011
https://dl.acm.org/doi/10.1109/TCBB.2010.13

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Published In

cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 6, Issue 2

April 2009

191 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 April 2009

Published in TCBB Volume 6, Issue 2

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Cited By

Takenouchi TIshii S(2018)Binary classifiers ensemble based on Bregman divergence for multi-class classificationNeurocomputing10.1016/j.neucom.2017.08.004273:C(424-434)Online publication date: 17-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.08.004
Bicego MLovato PPerina AFasoli MDelledonne MPezzotti MPolverari AMurino V(2012)Investigating Topic Models' Capabilities in Expression Microarray Data ClassificationIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2012.1219:6(1831-1836)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1109/TCBB.2012.121
Saraswathi SSundaram SSundararajan NZimmermann MNilsen-Hamilton M(2011)ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly RepresentedIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2010.138:2(452-463)Online publication date: 1-Mar-2011
https://dl.acm.org/doi/10.1109/TCBB.2010.13

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