Authors: Jia Wu; Zhi-hua Cai; Xiao-lin Chen; Shuang Ao
Addresses: School of Computer Science, China University of Geosciences, No. 388 Lumo Road, Wuhan, 430074, China ' School of Computer Science, China University of Geosciences, No. 388 Lumo Road, Wuhan, 430074, China ' School of Computer Science, China University of Geosciences, No. 388 Lumo Road, Wuhan, 430074, China ' School of Computer Science, University of Western Ontario, London, Ontario N6A 5B7, Canada
Abstract: In this paper, we first present active Averaged One-Dependence Estimator (AODE) learning classification model, which can improve the performance of AODE by selecting and asking experts to label the samples only with maximum information. Several common sampling strategies for active learning are discussed. Unfortunately, these methods can get the outlier, which will lead to scale up the classification-reduced error and high complexity. Motivated by those analyses, we propose a new active learning strategy, which is based on the uncertainty sampling and classification accuracy loss sampling strategy. Experimental results on three UCI standard data sets and a real remote sensing data set show that the AODE classification model and our novel active learning strategy can get better classification accuracy with fewer labelled samples than that of the state-of-the-art approaches for active learning.
Keywords: active learning; Bayesian; AODE; averaged one-dependence estimator; uncertainty sampling; farthest-first sampling; remote sensing; classification accuracy.
International Journal of Computer Applications in Technology, 2013 Vol.47 No.4, pp.326 - 333
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 25 Jul 2013 *