ANN Rule Extraction using Evolutionary Programmed Fuzzy Membership Functions
نویسنده
چکیده
An algorithm is presented that uses evolutionary programming to construct fuzzy membership functions that are used to extract Zadeh-Mamdani fuzzy rules from a constructive neural network. The algorithm has potential applications in fields such as data mining and knowledge-based decision support systems. Evaluation of the algorithm over two well known benchmark data sets shows that while the results are promising, some problems are apparent. These problems provide avenues for further research.
منابع مشابه
Proposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...
متن کاملMultiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions
Multiobjective genetic fuzzy rule selection is based on the generation of a set of candidate fuzzy classification rules using a preestablished granularity or multiple fuzzy partitions with different granularities for each attribute. Then, a multiobjective evolutionary algorithm is applied to perform fuzzy rule selection. Since using multiple granularities for the same attribute has been sometim...
متن کاملOptimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining
The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since t...
متن کاملSimplifying the automatic design of a fuzzy logic controller using evolutionary programming
With the availability of a wide range of Evolutionary Algorithms such as Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of su...
متن کاملMulti - objective Evolutionary Membership Functions Tuning as a Post - processing Task in the Generation of Fuzzy Classification Systems
In multi-objective evolutionary fuzzy systems, the process of tuning the membership functions plays an important role towards optimizing the systems accuracy. Although, the shape and position of the membership functions in the partition should not change too much with relation to the original partition, so that it does not lose its integrity. This paper presents and discusses multi-objective ev...
متن کامل