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Artificial Intelligence
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Fuzzy lattice neurocomputing (FLN) models
Neural Networks
Learning and decision-making in the framework of fuzzy lattices
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Fuzzy Sets and Systems: Theory and Applications
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IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Fuzzy Systems
Learning in the framework of fuzzy lattices
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
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Fuzzy lattice neural network (FLNN): a hybrid model for learning
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
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Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
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This work introduces FINkNN, a k-nearest-neighbor classifier operating over the metric lattice of conventional interval-supported convex fuzzy sets. We show that for problems involving populations of measurements, data can be represented by fuzzy interval numbers (FINs) and we present an algorithm for constructing FINs from such populations. We then present a lattice-theoretic metric distance between FINs with arbitrary-shaped membership functions, which forms the basis for FINkNN's similarity measurements. We apply FINkNN to the task of predicting annual sugar production based on populations of measurements supplied by Hellenic Sugar Industry. We show that FINkNN improves prediction accuracy on this task, and discuss the broader scope and potential utility of these techniques.