Evidential classification of incomplete instance based on K-nearest centroid neighbor

Abstract

Classification of incomplete instance is a challenging problem due to the missing features generally cause uncertainty in the classification result. A new evidential classification method of incomplete instance based on adaptive imputation thanks to the framework of evidence theory. Specifically, the missing values of different incomplete instances in test set are adaptively estimated based on Shannon entropy and K-nearest centroid neighbors (KNCNs) technology. The single or multiple edited instances (with estimations) then are classified by the chosen classifier to get single or multiple classification results for the instances with different discounting (weighting) factors, and a new adaptive global fusion method finally is proposed to unify the different discounted results. The proposed method can well capture the imprecision degree of classification by submitting the instances that are difficult to be classified into a specific class to associate the meta-class and effectively reduce the classification error rates. The effectiveness and robustness of the proposed method has been tested through four experiments with artificial and real datasets.