Weighted Conflict Evidence Combination Method Based on Hellinger Distance and the Belief Entropy

A Weighted Evidence Combination Method Based on Improved Conflict Measure Factor

D-S evidence theory is usually used for the fusion of multi-source information. But the fusion result is always against with general knowledge for the heavy conflict of evidence. Research on combination of conflict evidence at home and abroad is summarized and analyzed in detail. On the base of this, the conclusion that modified evidence combination method of conflict evidence is more useful ca...

Conflict management based on belief function entropy in sensor fusion

Wireless sensor network plays an important role in intelligent navigation. It incorporates a group of sensors to overcome the limitation of single detection system. Dempster-Shafer evidence theory can combine the sensor data of the wireless sensor network by data fusion, which contributes to the improvement of accuracy and reliability of the detection system. However, due to different sources o...

Hellinger distance

In this lecture, we will introduce a new notion of distance between probability distributions called Hellinger distance. Using some of the nice properties of this distance, we will generalize the fooling set argument for deterministic protocols to the randomized setting. We will then use this to prove a Ω(n) lower bound for the communication complexity of Disjointness. We will also see how this...

Class distribution estimation based on the Hellinger distance

Class distribution estimation (quantification) plays an important role in many practical classification problems. Firstly, it is important in order to adapt the classifier to the operational conditions when they differ from those assumed in learning. Additionally, there are some real domains where the quantification task is itself valuable due to the high variability of the class prior probabil...

A Method for Defuzzication by Weighted Distance