MetaCost

Overview

Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in data mining problems. Individually making each classification learner cost-sensitive is laborious, and often non-trivial. In this project we develop principled methods for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it.

Project members

• Pedro Domingos

Publications

• Domingos. MetaCost: A General Method for Making Classifiers Cost-Sensitive. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (KDD-99). 1999.
• Domingos. How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications. In Proceedings of AAAI-98/ICML-98 Workshop on the Methodology of Applying Machine Learning. 1998