Combining machine learned and heuristic rules using GRDR for detection of honeycombing in HRCT lung images

Authors:
Pramod K. Singh;Paul Compton
Affiliations:
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

Citing 4
Cited 1

A philosophical basis for knowledge acquisition

Knowledge Acquisition
"Boosting'' a Positive-Data-Only Learner

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Extension of the RDR Method That Can Adapt to Environmental Changes and Acquire Knowledge from Both Experts and Data

PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Combining knowledge acquisition and machine learning to control dynamic systems

IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2

Incremental knowledge acquisition using generalised RDR for soccer simulation

PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services

Quantified Score

Hi-index	0.00

Visualization

Abstract

A knowledge based system for detection of honeycombing patterns in HRCT lung images is described. In the system, rules generated by machine learning on low level image pixel-based features and heuristic rules from the domain expert on high level region-based features are combined using a generalized ripple down rules (GRDR) framework. Results demonstrate that the systems' performance can be incrementally improved.