Other Adaptations

Balasubramanian, V N and Lade, P and Venkateswara, H and Smirnov, E and Panchanathan, S (2014) Other Adaptations. In: Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications. Morgan Kaufmann, pp. 167-185. ISBN 978-0-12-398537-8

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Abstract

In this chapter, we describe three other adaptations of the CP framework, each of which is non-traditional in its own way. The task of obtaining a reliability value for the classification of a data instance has been the focus of a number of studies. In Sections 9.2 and 9.3, we describe two methods that use the idea of a metaclassifier to associate reliability values with output predictions from a base classifier. In particular, in Section 9.2, we describe the Metaconformal Predictors, where a base classifier is combined with a metaclassifier that is trained on metadata generated from the data instances and the classification results of the base classifier to associate reliability values on the classification of data instances. In Section 9.3, we describe the Single-Stacking Conformal Predictors, where an ensemble classifier consisting of the base classifier and the metaclassifier is constructed to compute reliability values on the classification outputs. The difference between the metaconformal and the single-stacking approaches is the manner in which the metadata are constructed and the way in which the reliability values are estimated.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Book Section
Additional Information: This chapter is based upon work supported by the US National Science Foundation under Grant No. 1116360. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the US National Science Foundation.
Uncontrolled Keywords: Ensemble Classifiers; Metaclassification; Metaconformal Predictors; ROC Curves; Single-Stacking Conformal Predictors; Time Series Analysis
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 19 Nov 2014 11:26
Last Modified: 05 Apr 2017 04:46
URI: http://raiith.iith.ac.in/id/eprint/864
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