Nearest convex hull classification by using Lotka–Volterra recurrent neural networks

Abstract

Distance-based classifiers have been applied to many multi-class classification problems. The nearest convex hull classifier (NCHC) is a useful distance-based classifier. It assigns a test sample to the class that has the closest convex hull. This paper proposes a new algorithm to implement the NCHC. Considering an alternative interpretation of NCHC, the distance from the test sample to the convex hull of the training data in a certain class can be thought of as the reconstruction error. We propose an algorithm that uses neural networks to implement the NCHC. Our experimental results show that NCHC using Lotka–Volterra recurrent neural networks outperforms other classifiers in a whole.