A Comparative Analysis of Feature Extraction Methods for Classifying Colon Cancer Microarray Data
Loading...
Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
EAI Publishing - EAI Endorsed Transactions on Scalable Information Systems, 4(14), 1 – 6
Abstract
Feature extraction is a proficient method for reducing dimensions in the analysis and prediction of cancer classification.
Microarray procedure has shown great importance in fetching informative genes that needs enhancement in diagnosis.
Microarray data is a challenging task due to high dimensional-low sample dataset with a lot of noisy or irrelevant genes
and missing data. In this paper, a comparative study to demonstrate the effectiveness of feature extraction as a
dimensionality reduction process is proposed, and concludes by investigating the most efficient approach that can be used
to enhance classification of microarray. Principal Component Analysis (PCA) as an unsupervised technique and Partial
Least Square (PLS) as a supervised technique are considered, Support Vector Machine (SVM) classifier were applied on
the dataset. The overall result shows that PLS algorithm provides an improved performance of about 95.2% accuracy
compared to PCA algorithms.
Keywords: Dimensionality Reduction, Principal Component Analysis, Partial Least Square, Support Vector Machine