A machine learning based framework to identify unseen classes in open-world text classification

Abstract

Classical supervised machine learning (ML) follows the assumptions of closed-world learning. However, this assumption does not work in an open-world dynamic environment. Therefore, the automated systems must be able to discover and identify unseen instances. Open-world ML can deal with unseen instances and classes through a two-step process: (1) discover and classify unseen instances and (2) identify novel classes discovered in step (1). Most existing research on open-world machine learning (OWML) only focuses on step 1. However, performing step 2 is required to build intelligent systems. The proposed framework comprises three different but interconnected modules that discover and identify unseen classes. Our in-depth performance evaluation establishes that the proposed framework improves open accuracy by up to 8% compared to the state-of-the-art models.