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:
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:
Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical aplications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whist providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and diabetes datasets with positive results.