Abstract
Averaged One-Dependence Estimators (AODE) combines all Super Parent-One-Dependence Estimators (SPODEs) with ensemble learning strategy. AODE demonstrates good classification accuracy with very little extra computational cost. However, it ignores the dependences between attributes. In this paper, we propose aggregating extended one-dependence estimators named Weighted One-Dependence Forests (WODF) which splits each SPODE into multiple subtrees by attribute selection. WODF assigns the weight to every subtree with conditional mutual information. Extensive experiments and comparisons on 40 UCI data sets demonstrate that WODF outperforms AODE and state-of-the-art weighted AODE algorithms. Results also confirm that WODF provides an appropriate tradeoff between runtime efficiency and classification accuracy.
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