A self-organizing neural network for detecting novelties

Abstract

In order to detect new events, a system must support on-line learning, adapting to pattern dynamic characteristics. Studies of such adaptation have originated the novelty detection area, which aims at identifying unexpected or unknown patterns. These researches have motivated this work to propose the on-line and unsupervised Self-Organizing Novelty Detection (SONDE) neural network. In this network, the creation of new neurons points out novelties. Experiments evaluated the influence of SONDE parameters and their capability to detect novelty events. These evaluations considered the datasets Biomed, ALL-AML Leukemia and DLBCL. Results are compared to others from GWR.