Paper Info
Title
A CBR-based fuzzy decision tree approach for database classification
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.
Year
DOI
Venue
2010
10.1016/j.eswa.2009.04.062
Expert Syst. Appl.
Keywords
Field
DocType
database classification,cbr-based fuzzy decision tree,different database classification application,genetic algorithm,historic database,fuzzy decision tree,fuzzy decision rule,large historic data,data classification,classification,clustering,case-based reasoning,hybrid classification model,current data,case based reasoning,case base reasoning,decision rule
Hit rate,Data mining,Fuzzy classification,Classification Tree Method,Computer science,Artificial intelligence,Data classification,Case-based reasoning,Cluster analysis,Genetic algorithm,Machine learning,Database,Incremental decision tree
Journal
Volume
Issue
ISSN
37
1
Expert Systems With Applications
Citations 
PageRank 
References 
20
1.19
38
Authors
3
Name
Order
Citations
PageRank
Pei-Chann Chang11752109.32
Chin-Yuan Fan247328.27
Wei-Yuan Dzan3364.06