Novel Hybrid Learning Algorithms for Tuning ANFIS Parameters Using Adaptive Weighted PSO

2007 ◽  
Author(s):  
M. Aliyari Shoorehdeli ◽  
M. Teshnehlab ◽  
A. K. Sedigh
Keyword(s):  
Learning Algorithms ◽  
Hybrid Learning

A GENETIC PROGRAMMING-BASED LEARNING ALGORITHMS FOR PRUNING COST-SENSITIVE CLASSIFIERS

2012 ◽  
Vol 11 (02) ◽  
pp. 1250011 ◽  
Author(s):  
ZAHRA NIKDEL ◽  
HAMID BEIGY
Keyword(s):  
Decision Tree ◽  
Genetic Programming ◽  
Learning Algorithm ◽  
Fitness Function ◽  
Learning Algorithms ◽  
Hybrid Learning ◽  
Experimental Results ◽  
Programming Algorithm ◽  
Hybrid Learning Algorithm ◽  
Misclassification Costs

In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian.

Sign in / Sign up

Export Citation Format

Share Document