Abstract
Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for phenotype classification of many diseases. Our proposed phenotype classifier is an ensemble method with k-top-scoring decision rules. Each rule involves a number of genes, a rank comparison relation among them, and a class label. Current classifiers, which are also ensemble methods, consist of k-top-scoring decision rules. Some of these classifiers fix the number of genes in each rule as a triple or a pair. In this paper, we generalize the number of genes involved in each rule. The number of genes in each rule ranges from 2 to N, respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Our algorithm saves resources by combining shorter rules in order to build a longer rule. It converges rapidly toward its high-scoring rule list by implementing several heuristics. The parameter k is determined by applying leave-one-out cross validation to the training dataset.
| Original language | English |
|---|---|
| Article number | 5357431 |
| Pages (from-to) | 216-226 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews |
| Volume | 40 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2010 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Software
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering
Cite this
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Microarray data classifier consisting of k-top-scoring rank-comparison decision rules with a variable number of genes. / Yoon, Youngmi; Bien, Sangjay; Park, Sang Hyun.
In: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 40, No. 2, 5357431, 01.03.2010, p. 216-226.Research output: Contribution to journal › Article
TY - JOUR
T1 - Microarray data classifier consisting of k-top-scoring rank-comparison decision rules with a variable number of genes
AU - Yoon, Youngmi
AU - Bien, Sangjay
AU - Park, Sang Hyun
PY - 2010/3/1
Y1 - 2010/3/1
N2 - Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for phenotype classification of many diseases. Our proposed phenotype classifier is an ensemble method with k-top-scoring decision rules. Each rule involves a number of genes, a rank comparison relation among them, and a class label. Current classifiers, which are also ensemble methods, consist of k-top-scoring decision rules. Some of these classifiers fix the number of genes in each rule as a triple or a pair. In this paper, we generalize the number of genes involved in each rule. The number of genes in each rule ranges from 2 to N, respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Our algorithm saves resources by combining shorter rules in order to build a longer rule. It converges rapidly toward its high-scoring rule list by implementing several heuristics. The parameter k is determined by applying leave-one-out cross validation to the training dataset.
AB - Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for phenotype classification of many diseases. Our proposed phenotype classifier is an ensemble method with k-top-scoring decision rules. Each rule involves a number of genes, a rank comparison relation among them, and a class label. Current classifiers, which are also ensemble methods, consist of k-top-scoring decision rules. Some of these classifiers fix the number of genes in each rule as a triple or a pair. In this paper, we generalize the number of genes involved in each rule. The number of genes in each rule ranges from 2 to N, respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Our algorithm saves resources by combining shorter rules in order to build a longer rule. It converges rapidly toward its high-scoring rule list by implementing several heuristics. The parameter k is determined by applying leave-one-out cross validation to the training dataset.
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UR - http://www.scopus.com/inward/citedby.url?scp=77249083030&partnerID=8YFLogxK
U2 - 10.1109/TSMCC.2009.2036594
DO - 10.1109/TSMCC.2009.2036594
M3 - Article
VL - 40
SP - 216
EP - 226
JO - IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
JF - IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
SN - 1094-6977
IS - 2
M1 - 5357431
ER -