Multiclass microarray data classification based on  confidence evaluation.
Microarray technology is becoming a powerful tool for  clinical diagnosis, as it has potential to discover gene expression patterns  that are characteristic for a particular disease. To date, this possibility has  received much attention in the context of cancer research, especially in tumor  classification. However, most published articles have concentrated on the  development of binary classification methods while neglected ubiquitous  multiclass problems. Unfortunately, only a few multiclass classification  approaches have had poor predictive accuracy. In an effort to improve  classification accuracy, we developed a novel multiclass microarray data  classification method. First, we applied a "one versus rest-support vector  machine" to classify the samples. Then the classification confidence of each  testing sample was evaluated according to its distribution in feature space and  some with poor confidence were extracted. Next, a novel strategy, which we  named as "class priority estimation method based on centroid distance", was  used to make decisions about categories for those poor confidence samples. This  approach was tested on seven benchmark multiclass microarray datasets, with  encouraging results, demonstrating effectiveness and feasibility.
School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, China. yuhualong@just.edu.cn
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HL
Yu
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Gao
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Qin
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Zhao
D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D060388:Support Vector Machine