Fitness Functions in Genetic Programming for Classification with Unbalanced Data

Autor:	Grant Patterson, Mengjie Zhang
Rok vydání:	2007
Předmět:	Fitness function Fitness approximation business.industry Genetic programming Minority class Machine learning computer.software_genre Class imbalance Binary classification Benchmark (computing) Artificial intelligence Unbalanced data business computer Mathematics
Zdroj:	AI 2007: Advances in Artificial Intelligence ISBN: 9783540769262 Australian Conference on Artificial Intelligence
DOI:	10.1007/978-3-540-76928-6_90
Popis:	This paper describes a genetic programming (GP) approach to binary classification with class imbalance problems. This approach is examined on two benchmark and two synthetic data sets. The results show that when using the overall classification accuracy as the fitness function, the GP system is strongly biased toward the majority class. Two new fitness functions are developed to deal with the class imbalance problem. The experimental results show that both of them substantially improve the performance for the minority class, and the performance for the majority and minority classes is much more balanced.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::da696c8d8d614fb7ebf9f24f505a898f https://doi.org/10.1007/978-3-540-76928-6_90 Zobrazit plný text záznamu