A machine learning based framework to identify unseen classes in open-world text classification |
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Institution: | 1. School of Information Management, Central China Normal University, Wuhan, 430079, China;3. Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, China;4. Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China;5. School of Information Management, Wuhan University, Wuhan, 430072, China |
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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. |
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Keywords: | Open-world machine learning Unseen instances Novel classes Neighborhood blending Intelligent applications Open text classification |
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