TY - GEN
T1 - PyCP
T2 - 9th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2013
AU - Balasubramanian, Vineeth N.
AU - Baker, Aaron
AU - Yanez, Matthew
AU - Chakraborty, Shayok
AU - Panchanathan, Sethuraman
PY - 2013
Y1 - 2013
N2 - The Conformal Predictions framework is a new game-theoretic approach to reliable machine learning, which provides a methodology to obtain error calibration under classification and regression settings. The framework combines principles of transductive inference, algorithmic randomness and hypothesis testing to provide guaranteed error calibration in online settings (and calibration in offline settings supported by empirical studies). As the framework is being increasingly used in a variety of machine learning settings such as active learning, anomaly detection, feature selection, and change detection, there is a need to develop algorithmic implementations of the framework that can be used and further improved by researchers and practitioners. In this paper, we introduce PyCP, an open-source implementation of the Conformal Predictions framework that currently provides support for classification problems within transductive and Mondrian settings. PyCP is modular, extensible and intended for community sharing and development.
AB - The Conformal Predictions framework is a new game-theoretic approach to reliable machine learning, which provides a methodology to obtain error calibration under classification and regression settings. The framework combines principles of transductive inference, algorithmic randomness and hypothesis testing to provide guaranteed error calibration in online settings (and calibration in offline settings supported by empirical studies). As the framework is being increasingly used in a variety of machine learning settings such as active learning, anomaly detection, feature selection, and change detection, there is a need to develop algorithmic implementations of the framework that can be used and further improved by researchers and practitioners. In this paper, we introduce PyCP, an open-source implementation of the Conformal Predictions framework that currently provides support for classification problems within transductive and Mondrian settings. PyCP is modular, extensible and intended for community sharing and development.
KW - Conformal predictions
KW - Open-source software
UR - http://www.scopus.com/inward/record.url?scp=84894099948&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894099948&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41142-7_37
DO - 10.1007/978-3-642-41142-7_37
M3 - Conference contribution
AN - SCOPUS:84894099948
SN - 9783642411410
T3 - IFIP Advances in Information and Communication Technology
SP - 361
EP - 370
BT - Artificial Intelligence Applications and Innovations - 9th IFIPWG 12.5 International Conference, AIAI 2013, Proceedings
PB - Springer New York LLC
Y2 - 30 September 2013 through 2 October 2013
ER -