Minimalist Machine Learning: Binary Classification of Medical Datasets with Matrix Transformations

Abstract

This work introduces an innovative machine learning algorithm based on the minimalist machine learning paradigm, called matrix transformations bootstrap. Evaluated on 15 medical datasets, ranging from 3 to 1626 attributes, the methodology incorporates matrix transformations like rotation and shearing to improve dataset separation in binary classification tasks. Additionally, random feature selection is applied via the bootstrap method, resulting in two new attributes that can be visualized on the Cartesian plane while achieving substantial dimensionality reduction. The results show significant classification performance improvements over traditional algorithms like k-NN, SVM, Bayesian models, ensembles, neural networks, and logistic functions, evaluated using balanced accuracy, recall, and F1-score.