Soft Inference as a Voting Mechanism in k-Nearest Neighbors Clustering Algorithm

The rapid growth of IT systems that use artificial intelligence algorithms necessitates increasingly accurate methods. To handle data uncertainty, computer scientists can employ soft sets. One popular classification method in machine learning that utilizes the idea of proximity between data points is the k-NN algorithm. In this paper, we describe a modification to the k-NN algorithm that makes use of soft sets to take into account uncertainty in the classification process. This is achieved by introducing soft inference as a voting mechanism. The authors present a mathematical model with pseudocode for re-implementation purposes and demonstrate and discuss experimental results from conducted tests to show the effectiveness of the proposed approach.