Differential evolution and perceptron decision trees for classification tasks

Authors:
R. A. Lopes;A. R. R. Freitas;R. C. Pedrosa Silva;Frederico Gadelha Guimarães
Affiliations:
Graduate Program in Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil;Graduate Program in Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil;Graduate Program in Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil;Department of Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 10
Cited 0

Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Instance-Based Learning Algorithms

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Multivariate Decision Trees

Machine Learning
Enlarging the Margins in Perceptron Decision Trees

Machine Learning
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Radial Basis Functions

Radial Basis Functions
Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook
A system for induction of oblique decision trees

Journal of Artificial Intelligence Research
Inducing oblique decision trees with evolutionary algorithms

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Due to its predictive capacity and applicability in different fields, classification has been one of the most important tasks in data mining. In this task, the Perceptron Decision Trees (PDT) have been used with good results. Thus, this paper presents a Differential Evolution algorithm that evolves PDTs. Furthermore, we also present the concept of legitimacy which is used to reduce the costs of solution evaluation, a time consuming part of the algorithm. The experiments comparing our method with other seven well known classifiers, show that the proposed approach is competitive and has potential to build very accurate models. The best solutions found by it were the best ones in the majority of the tested databases.