C4.5: programs for machine learning
C4.5: programs for machine learning
cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Ant Colony Optimisation Classification for Gene Expression Data Analysis
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
IEEE Computational Intelligence Magazine
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Inducing decision trees with an ant colony optimization algorithm
Applied Soft Computing
Ant colony decision forest meta-ensemble
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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Decision tree induction has been widely used to generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized in advance or during the learning process. The commonly used method is to partition the attribute range into two or several intervals using single or a set of cut points. One inherent disadvantage in these methods is that the use of sharp cut points makes the induced decision trees sensitive to noise. To overcome this problem this paper presents an alternative method called adaptive discretization based on Ant Colony Decision Tree (ACDT) approach. Experimental results showed that, by using that methodology, better classification accuracy has been obtained in both training and testing data sets in majority of cases concerning the classical decision tree constructed by ants. It suggests that the robustness of decision trees could be improved by means of this approach.