Application of machine learning techniques for reducing retrieval time in large case-based reasoning systems

Authors:
Froylan Martinez;Leonardo Garrido
Affiliations:
Center for Intelligent Systems, Monterrey Institute of Technology, Monterrey, N.L., Mexico;Center for Intelligent Systems, Monterrey Institute of Technology, Monterrey, N.L., Mexico
Venue:
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Year:
2006

Citing 8
Cited 0

Case-based reasoning

Case-based reasoning
A case-based approach to intelligent information retrieval

SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing

Foundations of statistical natural language processing
Relevance feedback retrieval of time series data

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Case-Based Reasoning: Experiences, Lessons and Future Directions

Case-Based Reasoning: Experiences, Lessons and Future Directions
Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993

Machine Learning
Self-optimising CBR retrieval

ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Foundations of Soft Case-Based Reasoning

Foundations of Soft Case-Based Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In real environments case bases are usually very large, so it is often a slow process to obtain the right case that will work for a particular problem solving situation using case-based reasoning (CBR). It is important to reduce retrieval time in CBR systems, in order to give better response time to the final user. In this paper, we present a novel approach to reduce the retrieval time in CBR systems. The approach that we show in this paper is a combination of cluster and decision tree techniques. This combination makes possible to build indexing structures in an automatic way. Our experimental results, based on two public domain datasets, show that employing our new approach improves retrieval time in CBR systems without losing significant accuracy degree in large case bases.