A Novel Method for a Reliable Classifier using Gradients

초록

In this paper, we propose a new classification method to complement a naïve Bayesian classifier. This classifier assumes data distribution to be Gaussian, finds the discriminant function, and derives the decision curve. However, this method does not investigate finding the decision curve in much detail, and there are some minor problems that arise in finding an accurate discriminant function. Our findings also show that this method could produce errors when finding the decision curve. The aim of this study has therefore been to investigate existing problems and suggest a more reliable classification method. To do this, we utilize the gradient to find the decision curve. We then compare/analyze our algorithm with the naïve Bayesian method. Performance evaluation indicates that the average accuracy of our classification method is about 10% higher than naïve Bayes.

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