Open-World Classification or Machine Learning

(Open Classification or Open Learning)
A form of Lifelong Learning

Second Edition: "Lifelong Machine Learning." by Z. Chen and B. Liu, Morgan & Claypool, August 2018 (1st edition, 2016)

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:

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).

Created on Jan 24, 2018 by Bing Liu.