Journal of Computer Science & Systems Biology

Varayini Pankayatselvan

Publications

• Research Article
  A One-Dimensional PCA Approach for Classifying Imbalanced Data
  Author(s): Derrick KR and Varayini PankayatselvanDerrick KR and Varayini Pankayatselvan
  
  Background: Highly complex and computational intensive methods based on Synthetic Minority Over-sampling Technique (SMOTE) and more recently Learning Vector Quantization SMOTE (LVQ-SMOTE) have been proposed for classification problems of imbalanced biomedical data. This works presents a much simpler approach that is not computationally intensive and competes well with existing approaches. It uses principal component analysis (PCA) to generate a pseudo-variable as a linear combination of the features. From this one pseudo-variable, several classification methods are developed that classify directly based on very simple statistics. One method, the Mean Method (MM), classifies cases based on closeness to the means for the two classes from training data sets. When the number of features is very large, a feature reduction (FR) procedure is.. Read More»
  DOI: 10.4172/jcsb.1000165

  Abstract PDF