Hypothesis testing approach on noisy cases in RICAD

Jirapun Daengdej, Dickson Lukose

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


Enabling the database applications to perform intelligent records retrieval is one of the important issues in database research. From one perspective, this particular issue has also been investigated in artificial intelligence (AI) research. Case-Based Reasoning (CBR) is an approach in AI that focusses on a similar issue. CBR systems mainly try to find the most similar cases from their case bases, and propose their answers based on the found cases. However, the main problem with this approach is that noisy cases can directly affect the accuracy of proposed solutions. This problem can also occur in database applications, if they also intend to formulate the correct answer for their users rather than just retrieving the records. This paper reviews the current practice in CBR research, especially on how the CBR systems are dealing with the problem of noisy cases, and describes how RICAD deals with noisy cases.

Original languageEnglish
Number of pages8
Publication statusPublished - 1997
Externally publishedYes
EventIEEE Knowledge and Data Engineering Exchange Workshop 1997 - California, Newport Beach, United States of America
Duration: 4 Nov 19974 Nov 1997
https://ieeexplore.ieee.org/xpl/conhome/4982/proceeding (Proceedings)


ConferenceIEEE Knowledge and Data Engineering Exchange Workshop 1997
Abbreviated titleKDEX 1997
Country/TerritoryUnited States of America
CityNewport Beach
Internet address

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