Computational Efficiency of Parallel Distributed Genetic Fuzzy Rule Selection for Large Data Sets
نویسندگان
چکیده
Genetic fuzzy rule selection is a two-phase classification rule mining method. First a large number of candidate fuzzy rules are generated by an association rule mining technique. Then only a small number of generated rules are selected by a genetic algorithm. We have already proposed an idea of parallel distributed implementation of genetic fuzzy rule selection. In this paper, we examine its computational efficiency for large data sets through computational experiments using a cluster system.
منابع مشابه
Parallel distributed genetic fuzzy rule selection
Genetic fuzzy rule selection has been successfully used to design accurate and compact fuzzy rulebased classifiers. It is, however, very difficult to handle large data sets due to the increase in computational costs. This paper proposes a simple but effective idea to improve the scalability of genetic fuzzy rule selection to large data sets. Our idea is based on its parallel distributed impleme...
متن کاملUse of Pareto-Optimal and Near Pareto-Optimal Candidate Rules in Genetic Fuzzy Rule Selection
Genetic fuzzy rule selection is an effective approach to the design of accurate and interpretable fuzzy rule-based classifiers. It tries to minimize the complexity of fuzzy rule-based classifiers while maximizing their accuracy by selecting only a small number of fuzzy rules from a large number of candidate rules. One important issue in genetic fuzzy rule selection is the prescreening of candid...
متن کاملNEW CRITERIA FOR RULE SELECTION IN FUZZY LEARNING CLASSIFIER SYSTEMS
Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...
متن کاملDISTRIBUTED AND COLLABORATIVE FUZZY MODELING
In this study, we introduce and study a concept of distributed fuzzymodeling. Fuzzy modeling encountered so far is predominantly of a centralizednature by being focused on the use of a single data set. In contrast to this style ofmodeling, the proposed paradigm of distributed and collaborative modeling isconcerned with distributed models which are constructed in a highly collaborativefashion. I...
متن کاملParallel Genetic Algorithms for Tuning a Fuzzy Data Mining System
In previous work, we have described methods that we have developed for tuning a fuzzy data mining system for intrusion detection using a hierarchical genetic algorithm. Unfortunately, the genetic algorithm approach is very slow due to the computational cost of the evaluation function. In this paper, we describe parallel implementations of the genetic algorithm that were run on both a multiproce...
متن کامل