Basic classification algorithms induce a single model from training data. The interpretation of a model is relatively easy, while basic algorithms have limitations in achieving high accuracy. An instance misclassified by a model may be correctly predicted by another. Hybrid classification is a concept that employs basic classification algorithms for model induction and for data preprocessing. Misclassification instances are usually considered to be noise, yet those still may carry useful information for identifying the class values of some other instances. This study proposes hybrid classification algorithms in which training instances are filtered to build three models for prediction. Each testing instance is classified by exactly one of them. The algorithms involved in the proposed hybrid classification algorithms are decision tree induction and naïve Bayesian classifier. The testing results on twenty data sets demonstrate that our hybrid classification algorithms can significantly outperform the basic ones as well as the hybrid algorithm proposed in a previous study. The hybrid classification algorithms based on instance filtering achieve relatively high accuracy and maintain the easy interpretation of learning results.
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