Speeding up incremental wrapper feature subset selection with Naive Bayes classifier
Knowledge-Based Systems
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A fundamental challenge for developing a cost-sensitive Naïve Bayes method is how to effectively classify an instance based on the cost-sensitive threshold computed under the assumption of knowing the instance's true classification probabilities and the highly biased estimations of these probabilities by the Naïve Bayes method. To address this challenge, we develop a cost-sensitive Naïve Bayes method from a novel perspective of inferring the order relation (e.g., greater than or equal to, less than) between an instance's true classification probability of belonging to the class of interest and the cost-sensitive threshold. Our method learns and infers the order relation from the training data and classifies the instance based on the inferred order relation. We empirically show that our proposed method significantly outperforms major existing methods for turning Naïve Bayes cost-sensitive through experiments with UCI data sets and a real-world case study.