Learning Algorithm for Threshold Softmax Layer to Handle Unknown Class Problem

Neural network are mostly trained with predefined class training data in supervised learning. But, when unknown test data (other than predefined class) are classified by a trained neural network, they are always misclassified into predefined classes, thus misclassification rate of trained neural network increases. To tackle these problems, Threshold Softmax Layer (TSM) and learning algorithm is proposed. In which, a normalized probability of each output class of the neural network is calculated and a threshold value is updated for each class during threshold learning process. If the maximum normalized probability of test data does not cross threshold value of the corresponding class, we will classify test data into unknown class. This TSM layer with neural network is evaluated on three UCI benchmark dataset (Glass, Yeast and Wine quality) and successfully handles the unknown class problem with reduced misclassification error.