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.
|Number of pages||11|
|Journal||IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews|
|Publication status||Published - 2010 Mar|
Bibliographical noteFunding Information:
Manuscript received March 18, 2008; revised October 5, 2008 and June 1, 2009 and October 28, 2009. First published December 22, 2009; current version published February 18, 2010. This work was supported by the National Research Foundation of Korea funded by the Ministry of Science and Technology, Korea Government under Grant 2009-0083311. This paper was recommended by Associate Editor Y. Jin.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering