Proceedings of the third international conference on Genetic algorithms
C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Application of Genetic Programming to Induction of Linear Classification Trees
Proceedings of the European Conference on Genetic Programming
Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees
Proceedings of the European Conference on Genetic Programming
Selected Papers from AISB Workshop on Evolutionary Computing
A Dynamic Programming Based Pruning Method for Decision Trees
INFORMS Journal on Computing
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Using genetic algorithms to develop intelligent decision trees
Using genetic algorithms to develop intelligent decision trees
A Genetic Algorithm-Based Approach for Building Accurate Decision Trees
INFORMS Journal on Computing
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
An Optimal Constrained Pruning Strategy for Decision Trees
INFORMS Journal on Computing
Evolutionary model tree induction
Proceedings of the 2010 ACM Symposium on Applied Computing
Information Sciences: an International Journal
An improved boosting based on feature selection for corporate bankruptcy prediction
Expert Systems with Applications: An International Journal
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Classification trees are widely used in the data mining community. Typically, trees are constructed to try and maximize their mean classification accuracy. In this paper, we propose an alternative to using the mean accuracy as the performance measure of a tree. We investigate the use of various percentiles (representing the risk aversion of a decision maker) of the distribution of classification accuracy in place of the mean. We develop a genetic algorithm (GA) to build decision trees based on this new criterion. We develop this GA further by explicitly creating diversity in the population by simultaneously considering two fitness criteria within the GA. We show that our bicriterion GA performs quite well, scales up to handle large data sets, and requires a small sample of the original data to build a good decision tree.