Classification consistency analysis for bootstrapping gene selection
Consistency modelling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of operations such as classification, clustering, or gene selection on a training set is often found to be very different from the same operations on a testing set, presenting a se...
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| Veröffentlicht in: | Neural computing & applications 2007-10, Vol.16 (6), p.527-539 |
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| container_title | Neural computing & applications |
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| creator | SHAONING PANG HAVUKKALA, Ilkka YINGJIE HU KASABOV, Nikola |
| description | Consistency modelling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of operations such as classification, clustering, or gene selection on a training set is often found to be very different from the same operations on a testing set, presenting a serious consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, this paper proposes a new concept of classification consistency and applies it for microarray gene selection problem using a bootstrapping approach, with encouraging results. |
| doi_str_mv | 10.1007/s00521-007-0110-1 |
| format | Article |
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| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Learning and adaptive systems Mathematics Multivariate analysis Parametric inference Probability and statistics Sciences and techniques of general use Statistics |
| title | Classification consistency analysis for bootstrapping gene selection |
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