@inbook{3b5bdfa46bf14ca4971d890800f44456,
title = "Lymphoma Cancer Classification Using Genetic Programming with SNR Features",
abstract = "Lymphoma cancer classification with DNA microarray data is one of important problems in bioinformatics. Many machine learning techniques have been applied to the problem and produced valuable results. However the medical field requires not only a high-accuracy classifier, but also the in-depth analysis and understanding of classification rules obtained. Since gene expression data have thousands of features, it is nearly impossible to represent and understand their complex relationships directly. In this paper, we adopt the SNR (Signal-to-Noise Ratio) feature selection to reduce the dimensionality of the data, and then use genetic programming to generate cancer classification rules with the features. In the experimental results on Lymphoma cancer dataset, the proposed method yielded 96.6% test accuracy in average, and an excellent arithmetic classification rule set that classifies all the samples correctly is discovered by the proposed method.",
author = "Hong, {Jin Hyuk} and Cho, {Sung Bae}",
year = "2004",
doi = "10.1007/978-3-540-24650-3_8",
language = "English",
isbn = "3540213465",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "78--88",
editor = "Maarten Keijzer and Lucas, {Simon M.} and Ernesto Costa and Terence Soule and Una-May O{\textquoteright}Reilly",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}
