Genetic Programming for data classification: partitioning the search space

Authors:
Jeroen Eggermont;Joost N. Kok;Walter A. Kosters
Affiliations:
Universiteit Leiden, Leiden, The Netherlands;Universiteit Leiden, Leiden, The Netherlands;Universiteit Leiden, Leiden, The Netherlands
Venue:
Proceedings of the 2004 ACM symposium on Applied computing
Year:
2004

Citing 7
Cited 16

C4.5: programs for machine learning

C4.5: programs for machine learning
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications

Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Induction of Decision Trees

Machine Learning
Evolving Fuzzy Decision Trees with Genetic Programming and Clustering

EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Evolving Objects: A General Purpose Evolutionary Computation Library

Selected Papers from the 5th European Conference on Artificial Evolution
Genetic Programming for data classification: partitioning the search space

Proceedings of the 2004 ACM symposium on Applied computing

Genetic Programming for data classification: partitioning the search space

Proceedings of the 2004 ACM symposium on Applied computing
Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis

Genetic Programming and Evolvable Machines
Differentiating between individual class performance in genetic programming fitness for classification with unbalanced data

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

Expert Systems with Applications: An International Journal
A meta-heuristic approach for improving the accuracy in some classification algorithms

Computers and Operations Research
Drawing boundaries: using individual evolved class boundaries for binary classification problems

Proceedings of the 13th annual conference on Genetic and evolutionary computation
Predicting problem difficulty for genetic programming applied to data classification

Proceedings of the 13th annual conference on Genetic and evolutionary computation
A case for learning simpler rule sets with multiobjective evolutionary algorithms

RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Two layered Genetic Programming for mixed-attribute data classification

Applied Soft Computing
A balanced neural tree for pattern classification

Neural Networks
A framework for optimization of genetic programming evolved classifier expressions using particle swarm optimization

HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Lazy learning for multi-class classification using genetic programming

ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Let us know your decision: Pool-based active training of a generative classifier with the selection strategy 4DS

Information Sciences: an International Journal
Identity verification based on handwritten signatures with haptic information using genetic programming

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Identification of epilepsy stages from ECoG using genetic programming classifiers

Computers in Biology and Medicine
Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. We will show that these refinement methods improve the classification performance of our algorithms.