Open-environment Machine Learning
It tackles the problem of adapting machine learning to real-world, evolving scenarios for practitioners, but the abstract suggests it is an incremental overview rather than presenting new breakthroughs.
The paper addresses the challenge of machine learning in open environments where key factors change over time, such as new classes and shifting data distributions, and introduces advances in techniques to handle these dynamic conditions.
Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks, particularly those involving open-environment scenarios where important factors are subject to change, called open-environment machine learning (Open ML) in this article, are present to the community. Evidently it is a grand challenge for machine learning turning from close environment to open environment. It becomes even more challenging since, in various big data tasks, data are usually accumulated with time, like streams, while it is hard to train the machine learning model after collecting all data as in conventional studies. This article briefly introduces some advances in this line of research, focusing on techniques concerning emerging new classes, decremental/incremental features, changing data distributions, varied learning objectives, and discusses some theoretical issues.