C4.5: programs for machine learning
C4.5: programs for machine learning
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Multiple Ant-Colony Optimization for Network Routing
CW '02 Proceedings of the First International Symposium on Cyber Worlds (CW'02)
Simplifying decision trees: A survey
The Knowledge Engineering Review
An Extended Chi2 Algorithm for Discretization of Real Value Attributes
IEEE Transactions on Knowledge and Data Engineering
A Discretization Algorithm Based on a Heterogeneity Criterion
IEEE Transactions on Knowledge and Data Engineering
FCACO: fuzzy classification rules mining algorithm with ant colony optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Geographical Information Science
Identifying a land use change cellular automaton by Bayesian data assimilation
Environmental Modelling & Software
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This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom-up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation-based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This 'bottom-up' ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO-CA model has been used to simulate rural-urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO-CA method can have a better performance than the decision-tree CA method.