Evolving Fuzzy Rules with UCS: Preliminary Results
نویسندگان
چکیده
This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. FuzzyUCS combines the generalization capabilities of UCS with the good interpretability of fuzzy rules to evolve highly accurate and understandable rule sets. Fuzzy-UCS is tested on a large collection of real-world problems, and compared to UCS and three highly-used machine learning techniques: the decision tree C4.5, the support vector machine SMO, and the fuzzy boosting algorithm Fuzzy LogitBoost. The results show that Fuzzy-UCS is highly competitive with respect to the four learners in terms of performance, and that the fuzzy representation permits a much better understandability of the evolved knowledge. These promising results of the online architecture of Fuzzy-UCS allow for further research and application of the system to new challenging problems.
منابع مشابه
Decision Making with Uncertainty Information Based on Lattice-Valued Fuzzy Concept Lattice
For the processing of decision making with uncertainty information, this paper establishes a decision model based on lattice-valued logic and researches the algorithm for extracting the maximum decision rules. Firstly, we further research the lattice-valued fuzzy concept lattice by combining the lattice implication algebra and classical concept lattice; secondly, we define the lattice-valued de...
متن کاملApproximate Versus Linguistic Representation in Fuzzy-UCS
This paper introduces an approximate fuzzy representation to FuzzyUCS, a Michigan-style Learning Fuzzy-Classifier System that evolves linguistic fuzzy rules, and studies whether the flexibility provided by the approximate representation results in a significant improvement of the accuracy of the models evolved by the system. We test Fuzzy-UCS with both approximate and linguistic representation ...
متن کاملAn Integrated MFFP-tree Algorithm for Mining Global Fuzzy Rules from Distributed Databases
In the past, many algorithms have been proposed for mining association rules from binary databases. Transactions with quantitative values are, however, also commonly seen in real-world applications. Each transaction in a quantitative database consists of items with their purchased quantities. The multiple fuzzy frequent pattern tree (MFFP-tree) algorithm was thus designed to handle a quantitati...
متن کاملAutomatic Construction of Fuzzy Rule Bases: a further Investigation into two Alternative Inductive Approaches
The definition of the Fuzzy Rule Base is one of the most important and difficult tasks when designing Fuzzy Systems. This paper discusses the results of two different hybrid methods, previously investigated, for the automatic generation of fuzzy rules from numerical data. One of the methods, named DoC-based, proposes the creation of Fuzzy Rule Bases using genetic algorithms in association with ...
متن کاملIdentification of evolving fuzzy rule-based models
An approach to identification of evolving fuzzy rule-based (eR) models is proposed in this paper. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rule...
متن کامل