Case Consistency in Case-Based Reasoning
Abstract
Case-Based Reasoning (CBR) is a Machine Learning technique that models human reasoning. As it learns, however, it becomes slower as its case library grows. In addition, with noisy or changing data, it may learn incorrect data or need to remove old data from its memory. An algorithm is developed to reduce the size of a case library without reducing quality. Using this algorithm, whenever a CBR system learns new cases, it will compare these data with existing cases and determine whether old cases should be removed and or the new cases should be learned. The algorithm makes it's decision based on the age of the old cases, their similarity to the learning cases, and the amount of cases already learned in that area. This thesis describes the algorithm and a prototype built to evaluate its effectiveness.
Notes
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Thesis Completion
2005
Semester
Spring
Advisor
Wu, Annie
Degree
Bachelor of Science (B.S.)
College
College of Engineering and Computer Science
Degree Program
Computer Science
Subjects
Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic; Case based reasoning
Format
Identifier
DP0022091
Language
English
Access Status
Open Access
Length of Campus-only Access
None
Document Type
Honors in the Major Thesis
Recommended Citation
Rosander, Bryan, "Case Consistency in Case-Based Reasoning" (2005). HIM 1990-2015. 476.
https://stars.library.ucf.edu/honorstheses1990-2015/476