Extensions to the CART algorithm
International Journal of Man-Machine Studies
Highly regular, modular, and cascadable design of cellular automata-based pattern classifier
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on system-level interconnect prediction
Evolving Cellular Automata Based Associative Memory for Pattern Recognition
HiPC '01 Proceedings of the 8th International Conference on High Performance Computing
Evolving Cellular Automata as Pattern Classifier
ACRI '01 Proceedings of the 5th International Conference on Cellular Automata for Research and Industry
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Theory and application of cellular automata for pattern classification
Fundamenta Informaticae - Special issue on cellular automata
RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata
Fundamenta Informaticae - Special issue on DLT'04
Cellular Automata: Theory and Application in Artificial Intelligence
Fundamenta Informaticae - Membrane Computing
A new, cellular automaton-based, nearest neighbor pattern classifier and its VLSI implementation
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Design and characterization of cellular automata based associative memory for pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy–Rough Supervised Attribute Clustering Algorithm and Classification of Microarray Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Error correcting capability of cellular automata based associative memory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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From the view of a cell, the partition of a pattern space is a uniform partition. It is difficult to meet the needs of spatial non-uniform partitioning. In this paper, a cellular automaton classifier with a tree structure is proposed, by combining multiple-attractor cellular automata with the algorithm CART. The method of construction of the characteristic matrix of the multiple-attractor cellular automata is studied on the basis of particle swarm optimization. This method builds multiple-attractor cellular automata as tree nodes. This kind of classifier can be used to solve the non-uniform partition problem and obtain a good classification performance by using a pseudo-exhaustive field with a few bits, and so can restrain the over-fitting. The feasibility and the effectiveness of this method have been verified by experiments.