title: Robust classification ensemble method for microarray data.
abstract: The objective of this study is to develop an accurate and robust classification ensemble method suitable for microarray data with noises. We proposed an algorithm, pattern match (PM)-bagging, which performs well in accuracy and is robust to noise variables and noise observations. From the experiments with real data set, the performance of the proposed method is found quite comparable and not much degraded even when the data set has noise variables or noise observations, while some other ensemble methods showed degradations of performance. A bias and variance decomposition showed that the success of the proposed method is due to an effective reduction of both bias and variance.
authors.affiliation: Department of Statistics, University of Wisconsin-Madison, WI 53706, USA. chungdon@stat.wisc.edu
authors.forename: Dongjun
authors.initials: D
authors.lastname: Chung
authors.forename: Hyunjoong
authors.initials: H
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000145:classification / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods