Prediction using a symbolic based hybrid system
- Dazeley, Richard, Kang, Byeongho
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Full Text:
- Description: 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.
- Description: 2003006510
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Full Text:
- Description: 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.
- Description: 2003006510
The viability of prudence analysis
- Dazeley, Richard, Kang, Byeongho
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Full Text:
- Description: Prudence analysis (PA) is a relatively new, practical and highly innovative approach to solving the problem of brittleness. PA is essentially an incremental validation approach, where each situation or case is presented to the KBS for inferencing and the result is subsequently validated. Therefore, instead of the system simply providing a conclusion, it also provides a warning when the validation fails. This allows the user to check the solution and correct any potential deficiencies found in the knowledge base. There have been a small number of potentially viable approaches to PA published that show a high degree of accuracy in identifying errors. However, none of these are perfect, very rarely a case is classified incorrectly and not identified by the PA system. The work in PA thus far, has focussed on reducing the frequency of these missed warnings, however there has been no studies on the affect of these on the final knowledge base’s performance. This paper will investigate how these errors in a knowledge base affect its ability to correctly classify cases. The results in this study strongly indicate that the missed errors have a significantly smaller influence on the inferencing results than would be expected, which strongly support the viability of PA.
- Description: 2003006508
- Authors: Dazeley, Richard , Kang, Byeongho
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at Pacific Rim Knowledge Acquisition Workshop 2008, PKAW-08, Hanoi, Vietnam : 15th-16th December 2008
- Full Text:
- Description: Prudence analysis (PA) is a relatively new, practical and highly innovative approach to solving the problem of brittleness. PA is essentially an incremental validation approach, where each situation or case is presented to the KBS for inferencing and the result is subsequently validated. Therefore, instead of the system simply providing a conclusion, it also provides a warning when the validation fails. This allows the user to check the solution and correct any potential deficiencies found in the knowledge base. There have been a small number of potentially viable approaches to PA published that show a high degree of accuracy in identifying errors. However, none of these are perfect, very rarely a case is classified incorrectly and not identified by the PA system. The work in PA thus far, has focussed on reducing the frequency of these missed warnings, however there has been no studies on the affect of these on the final knowledge base’s performance. This paper will investigate how these errors in a knowledge base affect its ability to correctly classify cases. The results in this study strongly indicate that the missed errors have a significantly smaller influence on the inferencing results than would be expected, which strongly support the viability of PA.
- Description: 2003006508
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