Title :
Alpha-integration of multiple evidence
Author :
Choi, Heeyoul ; Katake, Anup ; Choi, Seungjin ; Choe, Yoonsuck
Author_Institution :
Dept. of Comput. Sci. & Eng., Texas A & M Univ., College Station, TX, USA
Abstract :
In pattern recognition, data integration is a processing method to combine multiple sources so that the combined result can be more accurate than a single source. Evidence theory is one of the methods that have been successfully applied to the data integration task. Since Dempster-Shafer theory as the first evidence theory can be against our intuitive reasoning with some data sets, many researchers have proposed different rules for evidence theory. Among all these rules, the averaging rule is known to be better than others. On the other hand, a-integration was proposed by Amari as a principled way of blending multiple positive measures. It is a generalized averaging algorithm including arithmetic, geometric and harmonic means as its special case. In this paper, we generalize evidence theory with α-integration. Our experimental results show how our proposed methods work.
Keywords :
arithmetic; data integrity; geometry; inference mechanisms; pattern recognition; stochastic processes; uncertainty handling; α-integration; alpha integration; arithmetic mean; averaging rule; data integration; evidence theory; geometric mean; harmonic mean; intuitive reasoning; multiple evidence; pattern recognition; Arithmetic; Bayesian methods; Clustering algorithms; Inference algorithms; Information resources; Neural networks; Pattern recognition; Probability distribution; Stochastic processes; Uncertainty; Data Integration; Evidence Theory;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495745