Representation of a granule, relation and operation between two granules are mainly researched in granular computing. Hyperbox granular computing classification algorithms (HBGrC) are proposed based on interval analysis. Firstly, a granule is represented as the hyperbox which is the Cartesian product of $N$ intervals for classification in the $N$-dimensional space. Secondly, the relation betwee...

A bottle up hyperbox granular computing (HBGrC) is developed based on distance measure. Firstly, hyperbox granule is represented by the beginning point and the end point. Secondly, the distance measure between two hyperbox granules is defined by the beginning points and the end points. Thirdly, operations between two hyperbox granules are designed to the transformation between two hyperbox gran...

Shape of granule is one of the important issues in granular computing classification problems and related to the classification accuracy, the number of granule, and the join process of two granules. A bottle up granular computing classification algorithm (BUGrC) is developed in the frame work of fuzzy lattices. Firstly, the granules are represented as 4 shapes, namely hyperdiamond granule, hype...

A strong hyperbox-respecting coloring of an n-dimensional hyperbox partition is a coloring of the corners of its hyperboxes with 2n colors such that any hyperbox has all the colors appearing on its corners. A guillotine-partition is obtained by starting with a single axis-parallel hyperbox and recursively cutting a hyperbox of the partition into two hyperboxes by a hyperplane orthogonal to one ...

Different neural networks related to Fuzzy min-max (FMM) has been studied and amongst all, Enhanced Fuzzy min-max (EFMM) neural network is most recent. For classification of patterns a new Enhanced Fuzzy Min-Max (EFMM) algorithm has been studied. The aim of EFMM is to improve the performance and minimize the restrictions that are possessed by original fuzzy min-max (FMM) network. Three heuristi...

Granular computing classification algorithms are proposed based on distance measures between two granules from the view of set. Firstly, granules are represented as the forms of hyperdiamond, hypersphere, hypercube, and hyperbox. Secondly, the distance measure between two granules is defined from the view of set, and the union operator between two granules is formed to obtain the granule set in...

Finding the intersection between a hyperbox and a hyperplane can be computationally expensive specially for high dimensional problems. Naive algorithms have an exponential complexity. A border node is a node (in the graph induced by the hyperbox) at or next to the intersection of the hyperbox and the hyperplane. The algorithm proposed in this paper implements a systematic way to efficiently gen...

The possibilities of using hyperboxes and the ant colony optimization (ACO) metaheuristic to clustering and classification are examined. A clustering method, called HACO (hyperbox clustering with ant colony optimization), is presented for classifying unlabeled data using hyperboxes and the ACO meta-heuristic. It acknowledges the topological information (inherently associated to classification) ...

A novel adaptive fuzzy min-max neural network classifier called AFMN is proposed in this paper. Combined with principle component analysis and adaptive genetic algorithm, this integrated system can serve as a supervised and real-time classification technique. Considering the loophole in the expansion-contraction process of FMNN and GFMN and the overcomplex network architecture of FMCN, AFMN mai...