Classification Tree-based Active Learning: A Wrapper Approach

arXiv:2404.09953v1 Announce Type: new
Abstract: Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes crucial to explore active learning methods for reducing the size of training sets while maintaining high accuracy. The aim is to select the optimal subset of data for labeling from an initial unlabeled set, ensuring precise prediction of outcomes. However, conventional active learning approaches are comparable to classical random …

abstract accuracy active learning aim arxiv classification cs.lg data explore machine machine learning stat.ml supervised machine learning train training tree type wrapper