Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Reduction of fuzzy control rules by means of premise learning - method and case study
Fuzzy Sets and Systems - Fuzzy systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification
Applied Soft Computing
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
On the use of the weighted fuzzy c-means in fuzzy modeling
Advances in Engineering Software
Knowledge discovery by a neuro-fuzzy modeling framework
Fuzzy Sets and Systems
Information Sciences: an International Journal
A review of instance selection methods
Artificial Intelligence Review
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Takagi-Sugeno fuzzy modeling incorporating input variables selection
IEEE Transactions on Fuzzy Systems
Input selection for nonlinear regression models
IEEE Transactions on Fuzzy Systems
Automatic Design of Hierarchical Takagi–Sugeno Type Fuzzy Systems Using Evolutionary Algorithms
IEEE Transactions on Fuzzy Systems
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The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.