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

Print

Identifier

DP0022091

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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