Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Genetic Programming and Evolvable Machines
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Feature construction and dimension reduction using genetic programming
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Pattern Analysis & Applications
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
This work introduces a new technique for features construction in classification problems by means of multi objective genetic programming (MOGP). The final goal is to improve the generalization ability of the final classifier. MOGP can help in finding solutions with a better generalization ability with respect to standard genetic programming as stated in [1]. The main issue is the choice of the criteria that must be optimized by MOGP. In this work the construction of new features is guided by two criteria: the first one is the entropy of the target classes as in [7] while the second is inspired by the concept of margin used in support vector machines.