A CBR-based fuzzy decision tree approach for database classification

Authors:
Pei-Chann Chang;Chin-Yuan Fan;Wei-Yuan Dzan
Affiliations:
Department of Information Management, Yuan Ze University, Taoyuan 32026, Taiwan, ROC;Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32026, Taiwan, ROC;Department of Naval Architecture, National Kaohsiung Marine University, Kaohsiung 81143, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Database classification suffers from two well-known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case-based reasoning technique, a fuzzy decision tree (FDT), and genetic algorithms (GAs) to construct a decision-making system for data classification in various database applications. The model is major based on the idea that the historic database can be transformed into a smaller case base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller case-based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated experimentally compared with other approaches on different database classification applications. The average hit rate of our proposed model is the highest among others.