Generating weighted fuzzy rules from relational database systems for estimating values using genetic algorithms

Authors:
Shyi-Ming Chen;Chung-Ming Huang
Affiliations:
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan;-
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2003

Citing 0
Cited 15

A roughness measure for fuzzy sets

Information Sciences—Informatics and Computer Science: An International Journal
An efficient method for estimating null values in relational databases

Knowledge and Information Systems
Hybridization of intelligent techniques and ARIMA models for time series prediction

Fuzzy Sets and Systems
A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values

Expert Systems with Applications: An International Journal
Estimating null values in relational database systems using automatic clustering and multiple regression techniques

Expert Systems with Applications: An International Journal
Estimating Null Values in Relational Database Systems Having Negative Dependency Relationships Between Attributes

Cybernetics and Systems
A novel approach for missing data processing based on compounded PSO clustering

WSEAS Transactions on Information Science and Applications
GENERATING AUTOMATIC FUZZY SYSTEM FROM RELATIONAL DATABASE SYSTEM FOR ESTIMATING NULL VALUES

Cybernetics and Systems
A roughness measure for fuzzy sets

Information Sciences: an International Journal
Optimized parameters for missing data imputation

PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Optimal design of TS fuzzy control system based on DNA-GA and its application

LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Missing value imputation based on data clustering

Transactions on computational science I
A robust missing value imputation method for noisy data

Applied Intelligence
An extension of rough approximation quality to fuzzy classification

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Missing data analyses: a hybrid multiple imputation algorithm using Gray System Theory and entropy based on clustering

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years, some methods have been proposed to estimate values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.