Please use this identifier to cite or link to this item:
Title: Improving classification performance of microarray analysis by feature selection and feature extraction methods
Authors: Sun, Jing
Keywords: Microarray datasets;Feature Extraction;Feature Selection;Principal Component Analysis;Partial Least Square;Quadratic Programming Feature Selection;minimum Redundancy- Maximum Relevant;Support Vector Machine;Random Forest;k-Nearest-Neighbor;Neural Network
Issue Date: 26-Oct-2016
Abstract: In this study, we compared two feature extraction methods (PCA, PLS) and seven feature selection methods (mRMR and its variations, MaxRel, QPFS) on four different classifiers (SVM, RF, KNN, NN). We use ratio comparison validation for PCA method and 10-folds cross validation method for both the feature extraction and feature selection methods. We use Leukemia data set and Colon data set to apply the combinations and measured accuracy as well as area under ROC. The results illustrated that feature selection and extraction methods can both somehow improve the performance of classification tasks on microarray data sets. Some combinations of classifier and feature preprocessing method can greatly improve the accuracy as well as the AUC value are given in this study.
Appears in Collections:Computational Sciences - Master's theses
Master's Theses

Files in This Item:
File Description SizeFormat 
Jing Sun thesis-final version.pdf1.14 MBAdobe PDFThumbnail

Items in LU|ZONE|UL are protected by copyright, with all rights reserved, unless otherwise indicated.