Improving classification performance of microarray analysis by feature selection and feature extraction methods

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.

Name thesis:degree_name

Master of Science (MSc) in Computational Sciences

Grantor dc:publisher

Laurentian University of Sudbury

Year dc:date.issued

2016

Author dc:creator

• Sun, Jing

• 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

Language dc:language.iso

en

Repository record dc:identifier.uri

https://laurentian.scholaris.ca/handle/10219/2880

OAI record oai:identifier

https://laurentian.scholaris.ca/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Alaurentian.scholaris.ca%3A10219%2F2880