Learning with an augmented (unknown) class using neural networks

The wide diversity of categories encountered in big data applications means certain classes will be labelled while many other classes will remain unlabelled. A classification domain would consequently consist of known source classes for which labelled samples are available, and unknown novel classes that do not have any labelled samples. Given the assumption that novel classes are far more prevalent than source classes, it is appropriate to define an encapsulating 'unknown' class for all such novel classes. To be practically useful, classification systems must classify over both the source and the unknown novel domains. Regularly available data often includes unlabelled samples that, intuitively, belong to both source and novel classes. Including scattered unlabelled data with source labelled data during model training provides tractable means to learn classification boundaries between source classes and the unknown class. Da et al. were the first to introduce this framework as learning with augmented class by exploiting unlabelled data or LACU [6]. In this work, we promote the LACU paradigm to the neural network research space with the first LACU-enabled neural model. Using simple neural architectures, our proposed method produces state-of-the-art results when compared to previously published LACU works. With neural networks more capable of handling large datasets, this work takes us one step closer to building big data classifiers capable of known and unknown classification. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative.