Classification of ternary data using the ternary Allen–Cahn system for small datasets

In this study, we present a classification method for ternary small data using the modified Allen–Cahn (tAC) system. The governing system is tAC equation with fidelity term, which keeps solution as close possible to given data. To solve apply an operator splitting method. We use implicit-explicit finite difference solving split equations. validate robust and superior performance of proposed numerical algorithm, perform comparison tests other widely used classifiers such logistic regression, decision tree, support vector machine, random forest, artificial neural network datasets.