title: Multi-level fuzzy min-max neural network classifier.
abstract: In this paper a multi-level fuzzy min-max neural network classifier (MLF), which is a supervised learning method, is described. MLF uses basic concepts of the fuzzy min-max (FMM) method in a multi-level structure to classify patterns. This method uses separate classifiers with smaller hyperboxes in different levels to classify the samples that are located in overlapping regions. The final output of the network is formed by combining the outputs of these classifiers. MLF is capable of learning nonlinear boundaries with a single pass through the data. According to the obtained results, the MLF method, compared to the other FMM networks, has the highest performance and the lowest sensitivity to maximum size of the hyperbox parameter (θ), with a training accuracy of 100% in most cases.
authors.forename: Reza
authors.initials: R
authors.lastname: Davtalab
authors.forename: Mir Hossein
authors.initials: MH
authors.lastname: Dezfoulian
authors.forename: Muharram
authors.initials: M
authors.lastname: Mansoorizadeh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D017143:Fuzzy Logic; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer