International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Foundations of genetic algorithms
Foundations of genetic algorithms
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Evolution in Medical Decision Making
Journal of Medical Systems
Incremental Induction of Decision Trees
Machine Learning
Inductive Genetic Programming with Decision Trees
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Breeding Decision Trees Using Evolutionary Techniques
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees
Proceedings of the European Conference on Genetic Programming
Fuzzy Decision Trees in the Support of Breastfeeding
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
GA Tree: genetically evolved decision trees
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
A Genetic Algorithm-Based Approach for Building Accurate Decision Trees
INFORMS Journal on Computing
Evolutionary Induction of Decision Trees for Misclassification Cost Minimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
LEGAL-tree: a lexicographic multi-objective genetic algorithm for decision tree induction
Proceedings of the 2009 ACM symposium on Applied Computing
Artificial Intelligence in Medicine
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Artificial Intelligence in Medicine
Autonomous evolutionary algorithm in medical data analysis
Computer Methods and Programs in Biomedicine
Knowledge discovery with classification rules in a cardiovascular dataset
Computer Methods and Programs in Biomedicine
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Better Decision Tree from Intelligent Instance Selection: A new instance selection method based on Genetic Algorithm for optimizing decision trees
Evolutionary model tree induction
Proceedings of the 2010 ACM Symposium on Applied Computing
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
Lexicographic multi-objective evolutionary induction of decision trees
International Journal of Bio-Inspired Computation
Colon cancer prediction with genetics profiles using evolutionary techniques
Expert Systems with Applications: An International Journal
Globally induced model trees: an evolutionary approach
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Expert Systems with Applications: An International Journal
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Building cost-sensitive decision trees for medical applications
AI Communications
Information Sciences: an International Journal
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Decision trees (DT) are a type of data classifiers. A typical classifier works in two phases. In the first, the learning phase, the classifier is built according to a preexisting data (training) set. Because decision trees are being induced from a known training set, and the labels on each example are known the first step can also be referred to as supervised learning. The second step is when the induced classifier is used for classification. Usually, prior to the first step several steps should be performed to improve the accuracy and efficiency of the classification: data cleaning, redundancy elimination, and data normalization. Classifiers are evaluated for accuracy, speed, robustness, scalability, and interpretability. DTs are widely used for exploratory knowledge discovery where comprehensible knowledge representation is preferred. The main attraction of DTs lies in the intuitive representation that is easy to understand and comprehend. Accuracy, however, is dependent on the learning data. One of the methods to improve the induction and other phases in the creation of a classifier is the use of evolutionary algorithms. They are used because the classic deterministic approach is not necessarily optimal with regard to the quality, accuracy, and complexity of the obtained classifier. In addition to the description of different evolutionary DT induction approaches, this paper also presents multiple examples of evolutionary DT applications in the medical domain. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.