Sample reusability in importance-weighted active learning

Sample reusability in importance-weighted active learning

Van Tulder, G.

Loog, M. (mentor)

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

Pattern Recognition Lab

2012-10-31

Recent advances in importance-weighted active learning solve many of the problems of traditional active learning strategies. But does importance-weighted active learning also produce a reusable sample selection? This thesis explains why reusability can be a problem, how importance-weighted active learning removes some of the barriers to reusability and which obstacles still remain. With theoretical arguments and practical demonstrations, this thesis argues that universal reusability is impossible: because every active learning strategy must undersample some areas of the sample space, classifiers that depend on the samples in those areas will learn more from a random sample selection. This thesis describes several reusability experiments with importance-weighted active learning that show the impact of the reusability problem in practice. The experiments confirm that universal reusability does not exist, although in some cases – on some datasets and with some pairs of classifiers – there is sample reusability. This thesis explores the conditions that could guarantee the reusability between two classifiers.

active learning
importance-weighted active learning
sample reusability
importance weighting
machine learning

http://resolver.tudelft.nl/uuid:af4f9074-774e-4ff9-bab2-b58970b1c990

Student theses

master thesis

(c) 2012 Van Tulder, G.