Nonlinear classification, linear clustering, evolutionary semi-supervised three-way decisions: A comparison

This paper compares the semantically meaningful machine learning algorithms with the black box models. The machine learning models are applied to a real world wearable dataset for biometric identification of individuals. The semantically meaningful decision tree is compared with more accurate black-box models such as neural networks, random forest, and support vector machines. The paper further explores the possibility of using unsupervised learning that uses linear distances for separating the categories. Since the distance from the center is used to delineate the clusters, the centroids of the unsupervised clusters provide a semantic profile of the categories. The crisp K-means clustering is enhanced with evolutionary algorithms that primarily uses the distance from the center as the primary criteria, but nudges the clustering towards known classification using a semi-supervised penalty. Finally, the use of rough sets is shown to provide notable semantic information with the help of the three-way decision principle.