Title :
Grading Cost Sensitive Models
Author :
Kotsiantis, Sotiris ; Kanellopoulos, Dimitris
Author_Institution :
Dept. of Comput. Sci. & Technol., Univ. of Peloponnese, Tripoli
Abstract :
A learner induced from an imbalanced dataset has a low error rate for the majority class and an undesirable error rate for the minority class. This paper provides a study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed grading cost-sensitive ensemble and it concludes that this ensemble is a more effective solution to the problem.
Keywords :
learning (artificial intelligence); pattern classification; error rate; grading cost-sensitive ensemble; imbalanced dataset; Bayesian methods; Classification tree analysis; Computer science; Costs; Decision trees; Error analysis; Information technology; Machine learning; Testing; Training data; classification; data mining; imbalanced dataset; machine learning;
Conference_Titel :
Convergence and Hybrid Information Technology, 2008. ICCIT '08. Third International Conference on
Conference_Location :
Busan
Print_ISBN :
978-0-7695-3407-7
DOI :
10.1109/ICCIT.2008.103
