Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2018

Abstract

In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it is nearly impossible and highly costly to label all of the instances. In this article, we address this problem with active learning with expert advice, where the ground truth of an instance is disclosed only when it is requested by the proposed active query strategies. Our goal is to minimize the number of requests while training an online learning model without sacrificing the performance. To address this challenge, we propose a framework of active forecasters, which attempts to extend two fully supervised forecasters, Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of online active learning (OAL) with expert advice. Specifically, we proposed two OAL with expert advice algorithms, named Active Exponentially Weighted Average Forecaster (AEWAF) and active greedy forecaster (AGF), by considering the difference of expert advices. To further improve the robustness of the proposed AEWAF and AGF algorithms in the noisy scenarios (where noisy experts exist), we also proposed two robust active learning with expert advice algorithms, named Robust Active Exponentially Weighted Average Forecaster and Robust Active Greedy Forecaster. We validate the efficacy of the proposed algorithms by an extensive set of experiments in both normal scenarios (where all of experts are comparably reliable) and noisy scenarios.

Keywords

Online learning, active learning, expert advice, data streaming

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Knowledge Discovery from Data

Volume

12

Issue

5

First Page

1

Last Page

22

ISSN

1556-4681

Identifier

10.1145/3201604

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3201604

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