Guiding Genetic Program Based Data Mining Using Fuzzy Rules
نویسندگان
چکیده
A data mining procedure for automatic determination of fuzzy decision tree structure using a genetic program is discussed. A genetic program (GP) is an algorithm that evolves other algorithms or mathematical expressions. Methods for accelerating convergence of the data mining procedure are examined. The methods include introducing fuzzy rules into the GP and a new innovation based on computer algebra. Experimental results related to using computer algebra are given. Comparisons between trees created using a genetic program and those constructed solely by interviewing experts are made. Connections to past GP based data mining procedures for evolving fuzzy decision trees are established. Finally, experimental methods that have been used to validate the data mining algorithm are discussed.
منابع مشابه
Fuzzy Rule Selection By Data Mining Criteria And Genetic Algorithms
This paper shows how a small number of fuzzy rules can be selected for designing interpretable fuzzy rule-based classification systems. Our approach consists of two phases: candidate rule generation by data mining criteria and rule selection by genetic algorithms. First a large number of candidate rules are generated and prescreened using two rule evaluation criteria in data mining. Next a smal...
متن کاملGenetic program based data mining of fuzzy decision trees and methods of improving convergence and reducing bloat
A data mining procedure for automatic determination of fuzzy decision tree structure using a genetic program (GP) is discussed. A GP is an algorithm that evolves other algorithms or mathematical expressions. Innovative methods for accelerating convergence of the data mining procedure and reducing bloat are given. In genetic programming, bloat refers to excessive tree growth. It has been observe...
متن کاملAn Improved Algorithm for Fuzzy Data Mining for Intrusion Detection
We have been using fuzzy data mining techniques to extract patterns that represent normal behavior for intrusion detection. In this paper we describe a variety of modifications that we have made to the data mining algorithms in order to improve accuracy and efficiency. We use sets of fuzzy association rules that are mined from network audit data as models of " normal behavior. " To detect anoma...
متن کاملGenetic algorithm based framework for mining fuzzy association rules
It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining, simply because characteristics of quantitative data are in general unknown. Besides, it is unrealistic that the most appropriate fuzzy sets can always be provided by domain experts. Motivated by this, in this paperwe propose an automatedme...
متن کاملIntrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening
The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier....
متن کامل