Open-World Classification or Machine Learning
(Open Classification or Open Learning)
A form of Lifelong Learning
"Lifelong Machine Learning."
by Z. Chen and B. Liu, Morgan & Claypool, August 2018 (1st edition, 2016)
- Three new chapters have been added and others have been updated and/or reorganized.
- One Chapter is dedicated to open world learning
- A chatbot or self-driving car cannot learn during chatting or driving in the open envoronment is not intelligent.
Open world machine learning (a.k.a. open world classification or open classification) is getting increasingly important as the learning agent is increasingly working in the real-world open and dynamic environment, e.g., chatbot and self-driving car, where the agent cannot expect
what it will see in the real-world are what it has learned previously. The real-world is full of unknowns.
Classic machine learning makes the closed world assumption, i.e., the classes that the agent sees in training are what it will see in testing (no new classes can appear in testing) (Fei and Liu 2016). A more realistic scenario is to expect unseen classes during testing (open world). In this case, the goal is to design a learning algorithm that can classify data of the known classes into their respective classes and also to reject/detect instances from unknown classes. This problem is called open-world classification (or open-world learning). Apart from detecting the unseen classes, we also want to incrementally learn the new classes. An open world learner should be able to do the following:
- traditional close-world classification (assuming test instances are from only the training/seen classes),
- detecting or rejecting instances of unseen classes - not seen in training (the DOC algorithm (EMNLP-2017) is very powerful for this task for both text and images),
- autmatically detecting unseen classes in the rejected instances based on the knowledge learned in the past (Shu et al., 2018), and
- incrementally learning the new/unseen classes (Fei et al., 2016).
In the process, the system accumulates more and more knowledge (Fei et al., 2016).
The learner is self-motivated and it knows what it does and does not know. With intelligent systems such as chatbots and self-driving cars increasingly facing the real-world open (unknown) environments, we can no longer make the closed world assumption.
TextBook: Zhiyuan Chen and Bing Liu. Lifelong Machine Learning. Morgan & Claypool, 2018 (2nd edition), 2016 (1st edition).
- Hu Xu, Bing Liu, Lei Shu and P. Yu. Open World Learning for Product Classification. to appear in Proceedings of the Web Conference (formerly known as the WWW conference), San Francisco, May 13-17, 2019.
- Lei Shu, Hu Xu, Bing Liu. Unseen Class Discovery in Open-world Classification. arXiv:1801.05609 [cs.LG], 2018.
- Lei Shu, Hu Xu, Bing Liu. DOC: Deep Open Classification of Text Documents. Proceedings of 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP-2017, oral presentation short paper), September 7–11, 2017, Copenhagen, Denmark.
- Geli Fei, Shuai Wang, and Bing Liu. 2016. Learning Cumulatively to Become More Knowledgeable. Proceedings of SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2016), August 13-17, San Francisco, USA.
- Geli Fei, and Bing Liu. 2016. Breaking the Closed World Assumption in Text Classification. Proceedings of NAACL-HLT 2016 , June 12-17, San Diego, USA.
Created on Jan 24, 2018 by Bing Liu.