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Effects of Pooling Samples on the Performance of Classification Algorithms: A Comparative Study
- Source :
- The Scientific World Journal, Vol 2012 (2012), The Scientific World Journal
- Publication Year :
- 2012
- Publisher :
- Hindawi Limited, 2012.
-
Abstract
- A pooling design can be used as a powerful strategy to compensate for limited amounts of samples or high biological variation. In this paper, we perform a comparative study to model and quantify the effects of virtual pooling on the performance of the widely applied classifiers, support vector machines (SVMs), random forest (RF),k-nearest neighbors (k-NN), penalized logistic regression (PLR), and prediction analysis for microarrays (PAMs). We evaluate a variety of experimental designs using mock omics datasets with varying levels of pool sizes and considering effects from feature selection. Our results show that feature selection significantly improves classifier performance for non-pooled and pooled data. All investigated classifiers yield lower misclassification rates with smaller pool sizes. RF mainly outperforms other investigated algorithms, while accuracy levels are comparable among all the remaining ones. Guidelines are derived to identify an optimal pooling scheme for obtaining adequate predictive power and, hence, to motivate a study design that meets best experimental objectives and budgetary conditions, including time constraints.
- Subjects :
- Article Subject
Computer science
Pooling
lcsh:Medicine
Feature selection
computer.software_genre
Logistic regression
Machine learning
lcsh:Technology
General Biochemistry, Genetics and Molecular Biology
lcsh:Science
General Environmental Science
business.industry
lcsh:T
Design of experiments
lcsh:R
Computational Biology
General Medicine
Random forest
Support vector machine
Statistical classification
lcsh:Q
Data mining
Artificial intelligence
business
computer
Classifier (UML)
Algorithms
Research Article
Subjects
Details
- Language :
- English
- Volume :
- 2012
- Database :
- OpenAIRE
- Journal :
- The Scientific World Journal
- Accession number :
- edsair.doi.dedup.....b0f0bf3573c8e7696b30086859351459