An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data
Background: We consider both univariate- and multivariate-based feature selection for the problem of binary classification with microarray data. The idea is to determine whether the more sophisticated multivariate approach leads to better misclassification error rates because of the potential to con...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2006-01-01
|
| Series: | Cancer Informatics |
| Subjects: | |
| Online Access: | http://la-press.com/article.php?article_id=94 |
| _version_ | 1818286913807712256 |
|---|---|
| author | Michael Lecocke Kenneth Hess |
| author_facet | Michael Lecocke Kenneth Hess |
| author_sort | Michael Lecocke |
| collection | DOAJ |
| description | Background: We consider both univariate- and multivariate-based feature selection for the problem of binary classification with microarray data. The idea is to determine whether the more sophisticated multivariate approach leads to better misclassification error rates because of the potential to consider jointly significant subsets of genes (but without overfitting the data).Methods: We present an empirical study in which 10-fold cross-validation is applied externally to both a univariate-based and two multivariate- (genetic algorithm (GA)-) based feature selection processes. These procedures are applied with respect to three supervised learning algorithms and six published two-class microarray datasets.Results: Considering all datasets, and learning algorithms, the average 10-fold external cross-validation error rates for the univariate-, single-stage GA- , and two-stage GA-based processes are 14.2%, 14.6%, and 14.2%, respectively. We also find that the optimism bias estimates from the GA analyses were half that of the univariate approach, but the selection bias estimates from the GA analyses were 2.5 times that of the univariate results.Conclusions: We find that the 10-fold external cross-validation misclassification error rates were very comparable. Further, we find that a two-stage GA approach did not demonstrate a significant advantage over a 1-stage approach. We also find that the univariate approach had higher optimism bias and lower selection bias compared to both GA approaches. |
| first_indexed | 2024-12-13T01:32:09Z |
| format | Article |
| id | doaj.art-b95046ebc10149b2aed1254946f7279c |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-13T01:32:09Z |
| publishDate | 2006-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-b95046ebc10149b2aed1254946f7279c2022-12-22T00:03:59ZengSAGE PublishingCancer Informatics1176-93512006-01-012313327An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray DataMichael LecockeKenneth HessBackground: We consider both univariate- and multivariate-based feature selection for the problem of binary classification with microarray data. The idea is to determine whether the more sophisticated multivariate approach leads to better misclassification error rates because of the potential to consider jointly significant subsets of genes (but without overfitting the data).Methods: We present an empirical study in which 10-fold cross-validation is applied externally to both a univariate-based and two multivariate- (genetic algorithm (GA)-) based feature selection processes. These procedures are applied with respect to three supervised learning algorithms and six published two-class microarray datasets.Results: Considering all datasets, and learning algorithms, the average 10-fold external cross-validation error rates for the univariate-, single-stage GA- , and two-stage GA-based processes are 14.2%, 14.6%, and 14.2%, respectively. We also find that the optimism bias estimates from the GA analyses were half that of the univariate approach, but the selection bias estimates from the GA analyses were 2.5 times that of the univariate results.Conclusions: We find that the 10-fold external cross-validation misclassification error rates were very comparable. Further, we find that a two-stage GA approach did not demonstrate a significant advantage over a 1-stage approach. We also find that the univariate approach had higher optimism bias and lower selection bias compared to both GA approaches.http://la-press.com/article.php?article_id=94cross-validationfeature selectionsupervised-learninggenetic algorithm |
| spellingShingle | Michael Lecocke Kenneth Hess An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data Cancer Informatics cross-validation feature selection supervised-learning genetic algorithm |
| title | An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data |
| title_full | An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data |
| title_fullStr | An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data |
| title_full_unstemmed | An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data |
| title_short | An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data |
| title_sort | empirical study of univariate and genetic algorithm based feature selection in binary classification with microarray data |
| topic | cross-validation feature selection supervised-learning genetic algorithm |
| url | http://la-press.com/article.php?article_id=94 |
| work_keys_str_mv | AT michaellecocke anempiricalstudyofunivariateandgeneticalgorithmbasedfeatureselectioninbinaryclassificationwithmicroarraydata AT kennethhess anempiricalstudyofunivariateandgeneticalgorithmbasedfeatureselectioninbinaryclassificationwithmicroarraydata AT michaellecocke empiricalstudyofunivariateandgeneticalgorithmbasedfeatureselectioninbinaryclassificationwithmicroarraydata AT kennethhess empiricalstudyofunivariateandgeneticalgorithmbasedfeatureselectioninbinaryclassificationwithmicroarraydata |