Gravitation based classification
Classification is one of the key issues in the fields of decision sciences and knowledge discovery. In this paper, we present a new classification method based on gravitational potential energy between particles. The basic principle of gravitation based classification (GBC) algorithm is to find the equilibrium condition of the classifier, which is modeled as a classifier line between two groups of fixed particles. In the proposed approach, the input data is a collection of masses, which interact with each other based on Newtons universal law of gravitation and the laws of motion. We present a convex formulation for this problem that always converges to a global optimum solution. The proposed method has been compared with some well-known classification approaches, and the results confirm the high performance of the proposed method.
classification gravitational potential energy machine learning