A Novel Probabilistic Neural Network with Unlearned-class Detection Based on Normal and Complementary Gaussian Mixture Models
Takayuki Mukaeda, Keisuke Shima · Transactions of the Society of Instrument and Control Engineers · 2020
General classification methods only involve consideration of learned classes, and do not cover undefined targets such as unintended characteristics in the learning process. This paper proposes a novel probabilistic neural network can treat unlearned class. The proposed method incorporates two types of probabilistic distribution: normal and complementary Gaussian distribution and can reach multi-class classification and unlearned class detection with a single network. In the experiments, artificial data and electromyogram (EMG) patterns were classified to demonstrate the capabilities of the proposed method. The results showed that the approach produces high performance for classification, and there were significant differences between the proposed and previous methods.