Cross-Entropy-Weighted Model-Selection Averaged One-Dependence Estimators

Model selection has been applied to AODE (Averaged One-Dependence Estimator) algorithms in previous studies, significantly improving its classification performance. This is particularly effective for high-dimensional and complex datasets, which improves both the accuracy and robustness of the algorithm. Nevertheless, current cross-validation-based risk minimization strategies are still relatively limited in their application to AODE algorithms. In addition, these strategies usually do not fully account for the differences between different attributes, so the model does not accurately capture the associations and differences between attributes, which has an impact on the overall classification effect. To this end, we propose a Cross-Entropy-Weighted Model-Selection AODE (ESAODE) algorithm, which introduces a novel weight-computing scheme: epsilon-smoothed reciprocal cross entropy. It assigns distinct weights to all ODE sub-models according to the cross entropy obtained when each One-Dependence Estimator (ODE) approximates the target distribution. Furthermore, we evaluate the weighted sub-models and determine the optimal one based on empirical metrics from leave-one-out cross-validation (LOOCV). The new algorithm combines cross-entropy-based weighting and model selection within the AODE framework. Experimental results indicate that the ESAODE algorithm effectively integrates the advantages of the weighting mechanism and model selection, and improves the ability to capture key features as well as the adaptability to different datasets.