Title: Active Learning for Regression Using Greedy Sampling,
Abstract: Regression problems are pervasive in real-world applications. Generally a
substantial amount of labeled samples are needed to build a regression model
with good generalization ability. However, many times it is relatively easy to
collect a large number of unlabeled samples, but time-consuming or expensive to
label them. Active learning for regression (ALR) is a methodology to reduce the
number of labeled samples, by selecting the most beneficial ones to label,
instead of random selection. This paper proposes two new ALR approaches based
on greedy sampling (GS). The first approach (GSy) selects new samples to
increase the diversity in the output space, and the second (iGS) selects new
samples to increase the diversity in both input and output spaces. Extensive
experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15
subjects on EEG-based driver drowsiness estimation, verified their
effectiveness and robustness.