Hierarchical dependency constrained averaged one-dependence estimators classifiers for hierarchical feature spaces

Wan, Cen and Freitas, A. (2020) Hierarchical dependency constrained averaged one-dependence estimators classifiers for hierarchical feature spaces. In: The 10th International Conference on Probabilistic Graphical Models, 23-25 September 2020, Aalborg, Denmark. (Unpublished)

Abstract

The Averaged One-Dependence Estimators classifier is a type of probabilistic graphical model that constructs an ensemble of one-dependence networks, using each feature in turn as a parent node for all other features, in order to estimate the distribution of the data. In this work, we propose two new types of Hierarchical dependency constrained Averaged One Dependence Estimators (Hie-AODE) algorithms, which consider the pre-defined parent-child relationship between features during the construction of individual one-dependence estimators, when coping with hierarchically structured features. Experiments with 28 real-world bioinformatics datasets showed that the proposed Hie-AODE methods obtained better predictive performance than the conventional AODE classifier, and enhanced the robustness against imbalanced class distributions.

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