- Title
- Prediction using a symbolic based hybrid system
- Creator
- Dazeley, Richard; Kang, Byeongho
- Date
- 2008
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61646
- Identifier
- vital:3714
- Abstract
- Knowledge Based Systems (KBS) are highly successful in classification and diagnostics situations; however, they are generally unable to identify specific values for prediction problems. When used for prediction they either use some form of uncertainty reasoning or use a classification style inference where each class is a discrete predictive value instead. This paper applies a hybrid algorithm that allows an expert’s knowledge to be adapted to provide continuous values to solve prediction problems. The method applied to prediction in this paper is built on the already established Multiple Classification Ripple-Down Rules (MCRDR) approach and is referred to as Rated MCRDR (RM). The method is published in a parallel paper in this workshop titled Generalisation with Symbolic Knowledge in Online Classification. Results indicate a strong propensity to quickly adapt and provide accurate predictions.
- Publisher
- Hanoi, Vietnam :
- Relation
- Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0802 Computation Theory and Mathematics; Knowledge based systems; Knowledge representation; Prediction,; Ripple-down rules
- Full Text
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