Tarek M. Hamdani, Mohamed A. Khabou, Adel M. Alimi, and Fakhri Karray
Pattern recognition, multiple classifier systems, multi-agent design, negotiation, confidence rate, stress rate
We develop an intelligent multi-classifier decision-making system for multi-class classification tasks. The proposed system called I-MDS (Intelligent Multiple Decision System) uses a dynamic scheme to combine the information provided by the individual classifiers and make a classification decision. The individual classifiers in the system are interconnected and use a negotiation scheme to come up with a unified decision. During the interactive and reactive negotiation process, individual classifiers are allowed to revaluate their confidence in their individual decisions and to respond to a system-wise stress parameter that keeps increasing as long as the system does not reach a unified decision. If after a certain number of negotiation rounds the system can not reach a unified decision, the input pattern is rejected. The proposed systems were tested on multi-class classification problems from the UCI repository and were shown to produce better classification rates and fewer misclassifications than majority voting combination technique.
Important Links:
Go Back
