Minimum Displacement in Existing Moment (MDEM)- A New Supervised Learning Algorithm by Incrementally Constructing the Moments of the Underlying Classes

We propose a new supervised learning algorithm, where each data point in test set is included into the class into which such inclusion causes minimum displacement in the existing n-th central moment of the respective class under construction. 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, which is a popular dataset in machine learning research. Our analyses suggest that the performances of our proposed algorithms involving lower order moments are comparable to that of K-Nearest Neighbor (KNN) with a far better testing time complexity, while it is a bit under-performed as compared to Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) in terms of accuracy. Our analyses also suggest that our proposed algorithms also outperform the Neural Network (NN) models with relatively lower number of nodes and layers. However, if we continue to increase nodes and layers in the Neural Network, it tends to outperform the proposed algorithm. In a nutshell, 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.