@inproceedings{6a085eda79344ad48a76e74ecc3cf07b,
title = "Multi-class cancer classification with OVR-support vector machines selected by Na{\"i}ve bayes classifier",
abstract = "Support vector machines (SVMs), originally designed for binary classification, have been applied for multi-class classification, where an effective fusion scheme is required for combining outputs from them and producing a final result. In this work, we propose a novel method in which the SVMs are generated with the one-vs-rest (OVR) scheme and dynamically organized by the naive Bayes classifiers (NBs). This method might break the ties that frequently occur when working with multi-class classification systems with OVR SVMs. More specifically, we use the Pearson correlation measure to select informative genes and reduce the dimensionality of gene expression profiles when constructing the NBs. The proposed method has been validated on GCM cancer dataset consisting of 14 types of tumors with 16, 063 gene expression levels and produced higher accuracy than other methods.",
author = "Hong, {Jin Hyuk} and Cho, {Sung Bae}",
year = "2006",
doi = "10.1007/11893295_18",
language = "English",
isbn = "3540464840",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "155--164",
booktitle = "Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings",
address = "Germany",
note = "13th International Conference on Neural Information Processing, ICONIP 2006 ; Conference date: 03-10-2006 Through 06-10-2006",
}
