Estimation of distribution algorithms as logistic regression regularizers of microarray classifiers.
Objectives: The "large k (genes), small N (samples)" phenomenon complicates the problem of microarray classification with logistic regression. The indeterminacy of the maximum likelihood solutions, multicollinearity of predictor variables and data over-fitting cause unstable parameter estimates. Mor...
| Publicado en: | Methods of Information in Medicine Vol. 48; no. 3; pp. 236 - 242 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Thieme Medical Publishing Inc.
2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105536322&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105536322 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00261270 W7M jtl: Methods of Information in Medicine issn: 00261270 maglogo: N pubinfo: dt: 2009 vid: 48 iid: 3 pid: 2811 pub: Thieme Medical Publishing Inc. place: New York, New York artinfo: ui: 105536322 NLM19387512 2010286707 10.3414/ME9223 NLM19387512 105536322 ppf: 236 ppct: 6 formats: tig: atl: Estimation of distribution algorithms as logistic regression regularizers of microarray classifiers. aug: au: Bielza C Robles V Larranaga P Bielza, Concha Robles, V Larranaga, P affil: Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Spain sug: subj: Algorithms Biochips Classification Biochips Statistics and Numerical Data Logistic Regression Neoplasms Classification Neoplasms Human ab: Objectives: The "large k (genes), small N (samples)" phenomenon complicates the problem of microarray classification with logistic regression. The indeterminacy of the maximum likelihood solutions, multicollinearity of predictor variables and data over-fitting cause unstable parameter estimates. Moreover, computational problems arise due to the large number of predictor (genes) variables. Regularized logistic regression excels as a solution. However, the difficulties found here involve an objective function hard to be optimized from a mathematical viewpoint and a careful required tuning of the regularization parameters.Methods: Those difficulties are tackled by introducing a new way of regularizing the logistic regression. Estimation of distribution algorithms (EDAs), a kind of evolutionary algorithms, emerge as natural regularizers. Obtaining the regularized estimates of the logistic classifier amounts to maximizing the likelihood function via our EDA, without having to be penalized. Likelihood penalties add a number of difficulties to the resulting optimization problems, which vanish in our case. Simulation of new estimates during the evolutionary process of EDAs is performed in such a way that guarantees their shrinkage while maintaining their probabilistic dependence relationships learnt. The EDA process is embedded in an adapted recursive feature elimination procedure, thereby providing the genes that are best markers for the classification.Results: The consistency with the literature and excellent classification performance achieved with our algorithm are illustrated on four microarray data sets: Breast , Colon , Leukemia and Prostate . Details on the last two data sets are available as supplementary material.Conclusions: We have introduced a novel EDA-based logistic regression regularizer. It implicitly shrinks the coefficients during EDA evolution process while optimizing the usual likelihood function. The approach is combined with a gene subset selection procedure and automatically tunes the required parameters. Empirical results on microarray data sets provide sparse models with confirmed genes and performing better in classification than other competing regularized methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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