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Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines
Author(s) -
Peng Sihua,
Xu Qianghua,
Ling Xuefeng Bruce,
Peng Xiaoning,
Du Wei,
Chen Liangbiao
Publication year - 2003
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/s0014-5793(03)01275-4
Subject(s) - support vector machine , multiclass classification , class (philosophy) , algorithm , computer science , identification (biology) , artificial intelligence , microarray analysis techniques , feature (linguistics) , set (abstract data type) , machine learning , pattern recognition (psychology) , data mining , gene , biology , gene expression , genetics , linguistics , botany , philosophy , programming language
Simultaneous multiclass classification of tumor types is essential for future clinical implementations of microarray‐based cancer diagnosis. In this study, we have combined genetic algorithms (GAs) and all paired support vector machines (SVMs) for multiclass cancer identification. The predictive features have been selected through iterative SVMs/GAs, and recursive feature elimination post‐processing steps, leading to a very compact cancer‐related predictive gene set. Leave‐one‐out cross‐validations yielded accuracies of 87.93% for the eight‐class and 85.19% for the fourteen‐class cancer classifications, outperforming the results derived from previously published methods.