A convolutional fuzzy min-max neural network

Abstract

Data updation is a natural process in real life machine leaning problems. Online machine learning is a possible solution to tackle such new data and facilitate continuous updation of knowledge without hampering the already acquired one. This work proposes a convolutional fuzzy min-max neural network (CFMNN) for image classification with online learning ability. Image classification using convolutional neural network (CNN) is a well-known and established technique. The CNN based systems are trained in offline mode. To recognize new classes in addition to the learned ones, CNN requires a complete re-training based on earlier and new data samples. This training process of CNN is time consuming. The proposed CFMNN is designed to address this problem without the need of re-training. The CFMNN can learn new data classes in an online mode. In this work, the concept of hyperbox fuzzy set is introduced for CNN to add the online learning proficiency. Four different CNN architectures are considered to verify the robustness of CFMNN performance. The proposed method is evaluated on standard datasets MNIST, Caltech-101, Plant seedlings and CIFAR-100. Accuracy and training time are used as performance metrics. The experimental results show that CFMNN with online learning capability has tremendously reduced training time and it gives compatible or better accuracy than the existing methods.