Abstract:
We present a flexible, multi-agent approach to predictive classification
problems which uses simple, modular agents that interact and share
information socially in an arena with a variable number of participants.
Opinion aggregation is accomplished using a honey-bee-derived optimization
algorithm that improves accuracy and reduces variance compared with existing
weighted and unweighted voter mechanisms. Confidence metrics may be derived
from the agent interactions. We apply our system to a data set of 483
de-identified breast cancer patients to predict node-positive or
node-negative disease with over 78.5% accuracy in general. When eliminating
low-confidence predictions, which leaves 79.5% of patients, classification
accuracy improves to 84.5%.