Lazy learning for multi-class classification using genetic programming

Authors:
Hajira Jabeen;Abdul Rauf Baig
Affiliations:
Iqra University, Islamabad, Pakistan;National University of Computer and Emerging Sciences, Islamabad, Pakistan
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Year:
2011

Citing 10
Cited 0

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
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 for Multiple Class Object Detection

AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Genetic Programming for data classification: partitioning the search space

Proceedings of the 2004 ACM symposium on Applied computing
Evolving rule-based systems in two medical domains using genetic programming

Artificial Intelligence in Medicine
An autonomous GP-based system for regression and classification problems

Applied Soft Computing
Modifying genetic programming for artificial neural network development for data mining

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Evolving rule induction algorithms with multi-objective grammar-based genetic programming

Knowledge and Information Systems
Application of genetic programming for multicategory patternclassification

IEEE Transactions on Evolutionary Computation
A novel approach to design classifiers using genetic programming

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we have proposed a lazy learning mechanism for multiclass classification using genetic programming. This method is an improvement of traditional binary decomposition method for multiclass classification. We train classifiers for individual classes for a certain number of generations. Individual trained classifiers for each class are combined in a single chromosome. A population of such chromosomes is created and evolved further. This method suppresses the conflicting situations common in binary decomposition method. The proposed lazy learning method has performed better than traditional binary decomposition method over five benchmark datasets taken from UCI ML repository.