Discovering Cellular Automata Rules for Binary Classification Problem with Use of Genetic Algorithm

This paper proposes a cellular automata-based solution of a two-dimensional binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann neighborhood. Since the number of possible CA rules (potential CA-based classifiers)...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2012 26th IEEE International Parallel and Distributed Processing Symposium Workshops s. 649 - 655
Hlavní autoři: Piwonska, A., Seredynski, F., Szaban, M.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2012
Témata:
Automata
Boundary conditions
cellular automata
Computer science
Educational institutions
genetic algorithm
Genetic algorithms
Image color analysis
Training
two-dimensional binary classification problem
ISBN:1467309745, 9781467309745
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper proposes a cellular automata-based solution of a two-dimensional binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann neighborhood. Since the number of possible CA rules (potential CA-based classifiers) is huge, searching efficient rules is conducted with use of a genetic algorithm (GA). Experiments show an excellent performance of discovered rules in solving the classification problem. The best found rules perform better than the heuristic CA rule designed by a human and also better than one of the most widely used statistical method: the k-nearest neighbors algorithm (k-NN). Experiments show that CAs rules can be successfully reused in the process of searching new rules.
ISBN:1467309745
9781467309745
DOI:10.1109/IPDPSW.2012.81