Open-environment Machine Learning

TL;DR

This paper discusses the challenges and recent advances in open-environment machine learning, where data and conditions change over time, requiring new techniques beyond traditional static models.

Contribution

It provides an overview of recent research on open-environment machine learning, highlighting techniques for handling evolving data and scenarios.

Findings

Techniques for emerging new classes

Methods for decremental/incremental features

Approaches to changing data distributions

Abstract

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.