Generating artificial populations using a multi-level fuzzy inference engine
Proceedings of the 40th Conference on Winter Simulation
Multiple objects segmentation with fuzzy rule-base trained topology adaptive active membrane
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Large scale knowledge-based simulation models: an aproximation to the north-south remittances model
Winter Simulation Conference
Investigating a novel GA-based feature selection method using improved KNN classifiers
International Journal of Information and Communication Technology
Fuzzy rule-based approaches to dimensionality reduction
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
International Journal of Data Warehousing and Mining
Rule base identification in fuzzy networks by Boolean matrix equations
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Complexity management methodology for fuzzy systems with feedback rule bases
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Sammon's (1969) nonlinear projection method is computationally prohibitive for large data sets, and it cannot project new data points. We propose a low-cost fuzzy rule-based implementation of Sammon's method for structure preserving dimensionality reduction. This method uses a sample and applies Sammon's method to project it. The input data points are then augmented by the corresponding projected (output) data points. The augmented data set thus obtained is clustered with the fuzzy c-means (FCM) clustering algorithm. Each cluster is then translated into a fuzzy rule to approximate the Sammon's nonlinear projection scheme. We consider both Mamdani-Assilian and Takagi-Sugeno models for this. Different schemes of parameter estimation are considered. The proposed schemes are applied on several data sets and are found to be quite effective to project new points, i.e., such systems have good predictability