Investigation on enhancing the binary classification accuracy of supervised classifiers using various transforms

The Classification is used for testing instances where the unknown class labels are assigned where the predictor features are known. This paper aims to investigate the classification performance improvement of popular supervised classification approaches using data transformation techniques. Hilbert Transform, Discrete Wavelet Transform, and Principal Component Analysis are investigated as data transformation techniques for improving the performance of four different supervised classification approaches namely K-Nearest Neighbor classifier, Random Forest Classifier, Naive Bayes Classifier, and Support Vector Machine. SONAR dataset is used in this research work and the highest Mathews Correlation Coefficient of 0.72 is attained for Random Forest Classifier.