KNN-CF Approach: Incorporating Certainty Factor to kNN Classification

 Abstract—KNN classification finds k nearest neighbors of a query in training data and then predicts the class of the query as the most frequent one occurring in the neighbors. This is a typical method based on the majority rule. Although majority-rule based methods have widely and successfully been used in real applications, they can be unsuitable to the learning setting of skewed class distribution. This paper incorporates certainty factor (CF) measure to kNN classification, called kNN-CF classification, so as to deal with the above issue. This CF-measure based strategy can be applied on the top of a kNN classification algorithm (or a hot-deck method) to meet the need of imbalanced learning. This leads to that an existing kNN classification algorithm can easily be extended to the setting of skewed class distribution. Some experiments are conducted for evaluating the efficiency, and demonstrate that the kNN-CF classification outperforms standard kNN classification in accuracy.