Designing Storage Efficient Decision Trees
IEEE Transactions on Computers
C4.5: programs for machine learning
C4.5: programs for machine learning
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Machine Learning
Ant Colony Optimization
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
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
An adaptive discretization in the ACDT algorithm for continuous attributes
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
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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In this paper, we would like to propose a new method for constructing decision trees based on Ant Colony Optimization (ACO). The ACO is a metaheuristic inspired by the behavior of real ants, where they search for optimal solutions by considering both local heuristic and previous knowledge, observed by pheromone changes. Good results of the ant colony algorithms for solving combinatorial optimization problems suggest an appropriate effectiveness of the approach also in the task of constructing decision trees. In order to improve the accuracy of decision trees we propose an Ant Colony algorithm for constructing Decision Trees (ACDT). A heuristic function used in a new algorithm is based on the splitting rule of the CART algorithm (Classification and Regression Trees). The proposed algorithm is evaluated on a number of well-known benchmark data sets from the UCI Machine Learning repository. What deserves particular attention is the fact that empirical results clearly show that ACDT performs very good while comparing to other techniques.