Active learning in multiple-class classification problems via individualized binary models

Jingjing Li, Zimu Chen, Zhanfeng Wang and Yuan-chin Ivan Chang

Computational Statistics & Data Analysis, 2020, vol. 145, issue C

Abstract: We propose a unified algorithm for both categorical and ordinal labeled data in multiclass classification problems, where each subject belongs to one class only. In training an effective classification rule, it is critical that one have and rely on a sufficient amount of reliably labeled data. As information on the training sample sizes needed to obtain satisfactory performance is lacking, we adopt an adaptive subject recruiting scheme with an experimental design criterion to shorten the training process. Because this kind of active learning method is naturally conducted in a sequential manner, we adopt sequential analysis to control the required sample size and ensure the performance of the final classifier. Additionally, we report its statistical properties and numerical results from using synthesized and real data.

Keywords: Multiple-class classification; Sequential estimation; A-optimality design; Stopping time; Individualized binary estimation (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:145:y:2020:i:c:s0167947320300025

DOI: 10.1016/j.csda.2020.106911

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