Minimum Displacement in Existing Moment (MDEM)- A new supervised learning algorithm by incrementally constructing the moments of the underlying classes

Abstract

We introduce a supervised learning method that classifies each test point by selecting the class for which its inclusion causes minimum displacement of the class’s existing n-th central moment. After each such inclusion, the n-th central moment of the corresponding class is updated by some incremental calculations in constant time, i.e., each class evolves gradually and changes its definition incrementally after the inclusion of every new data point. We then use k-fold and stratified k-fold cross validation techniques to compare the performance of our proposed model with various state of the art supervised learning algorithms including Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Logistic Regression (LR) using Pima Indian Diabetes (PID) dataset and Wisconsin breast cancer dataset, which are popular datasets in machine learning research. Our analyses suggest the performances of different MDEM algorithms as proposed here involving different order of moments vary within the range of [83.19% − 95.82%] of the best algorithm under consideration in k-fold and stratified k-fold cross validation techniques for PID dataset. Moreover, for Wisconsin breast cancer dataset, different variants of MDEM algorithms have achieved accuracy scores in the range of [88.85% − 96.41%] of the best algorithm. Finally, we compare the results produced by different algorithms by constructing the corresponding confusion matrices.