Comparing Prediction Accuracy for Supervised Techniques in Gene Expression Data

Abstract

Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data classification etc. Microarray based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. The classification of different tumor types is of great significance in cancer diagnosis and drug innovation. One challenging area in the studies of gene expression data is the classification of different types of tumors into correct classes. Diagonal discriminant analysis, regularized discriminant analysis, support vector machines and k-nearest neighbor have been suggested as among the best methods for small sample size situations. The methods are applied to datasets from four recently published cancer gene expression studies. Four publicly available microarray data sets are Leukemia, Lymphoma, SRBCT & Prostate. The performance of the classification technique has been evaluated according to the percentage of misclassification through hold-out cross validation.