Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Clustering and Classification in Structured Data Domains Using Fuzzy Lattice Neurocomputing (FLN)
IEEE Transactions on Knowledge and Data Engineering
A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms
Journal of Intelligent Information Systems
The Journal of Machine Learning Research
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
International Journal of Approximate Reasoning
Computers & Mathematics with Applications
Fuzzy Sets and Systems
Information Sciences: an International Journal
Some remarks on the lattice of fuzzy intervals
Information Sciences: an International Journal
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A basis for rigorous versatile learning is introduced theoretically, that is the framework of fuzzy lattices or FL-framework for short, which proposes a synergetic combination of fuzzy set theory and lattice theory. A fuzzy lattice emanates from a conventional mathematical lattice by fuzzifying the inclusion order relation. Learning in the FL-framework can be effected by handling families of intervals, where an interval is treated as a single entity/block the way explained here. Illustrations are provided in a lattice defined on the unit-hypercube where a lattice interval corresponds to a conventional hyperbox. A specific scheme for learning by clustering is presented, namely σ-fuzzy lattice learning scheme or σ-FLL (scheme) for short, inspired from adaptive resonance theory (ART). Learning by the σ-FLL is driven by an inclusion measure σ of the corresponding Cartesian product to be introduced here. We delineate a comparison of the σ-FLL scheme with various neural-fuzzy and other models. Applications are shown to one medical data set and two benchmark data sets, where σ-FLL's capacity for treating efficiently real numbers as well as lattice-ordered symbols separately or jointly is demonstrated. Due to its efficiency and wide scope of applicability the σ-FLL scheme emerges as a promising learning scheme