Open AccessInvestigation on enhancing the binary classification accuracy of supervised classifiers using various transforms
Author(s) -
K. Issac,
Keshav Vaidyanathan Bharadwaj,
N. Bharanidharan,
Harikumar Rajaguru
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1084/1/012032
Subject(s) - pattern recognition (psychology) , naive bayes classifier , artificial intelligence , random forest , bayes classifier , classifier (uml) , computer science , quadratic classifier , principal component analysis , support vector machine , k nearest neighbors algorithm , linear classifier , wavelet transform , margin classifier , binary classification , random subspace method , machine learning , wavelet
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.