An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization

The main contribution of the present work is two-fold: (i) we describe a new general strategy to derive moment and exponential concentration inequalities for the LpO estimator

A fusion algorithm is proposed to combine the DBRB and KBRB to obtain an optimal hybrid belief rule base (HBRB), based on which a query pattern is classified by taking into

As illustrated in Section 2, the proposed CCW distance metric provides more local specifics in feature space than the CDW metric, so the sub-classifier with the correct hypothesis

Starting from an initial partition with as many clusters as objects in the dataset, the method, called EK-NNclus, iteratively applies the EK-NN rule to change the class labels, until

This suggests that the dependency between the combined classifiers may be complex enough to justify learning a complex, refined combination strategy, involving determining

In Section 3.2 below, we will solve these two issues by introducing a new version of the EKNN classifier suitable for partially supervised data, based on the combination of two

In this study, we propose a new classifier based on Dempster-Shafer (DS) theory and deep convolutional neural networks (CNN) for set-valued classification, called the evidential

Finally, we illustrate the validity of the method by demonstrating the convergence of the distributed data fusion algorithm using an operator based on the cautious rule