Active AODE learning based on a novel sampling strategy and its application

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.