Can I read Consistent Classification Algorithms for Multi-class Non-Decomposable Performance Metrics on EtoBox?

Consistent Classification Algorithms for Multi-class Non-Decomposable Performance Metrics by Ramaswamy, Harish G.; Narasimhan, Harikrishna; Agarwal, Shivani is a scholarly article available to read on EtoBox.

What is Consistent Classification Algorithms for Multi-class Non-Decomposable Performance Metrics about?

We study consistency of learning algorithms for a multi-class performance metric that is a non-decomposable function of the confusion matrix of a classifier and cannot be expressed as a sum of losses on individual data points; examples of such performance metrics include the macro F-measure popular in information retrieval and the G-mean metric used in class-imbalanced problems. While there has been much work in recent years in understanding the consistency properties of learning algorithms for `binary' non-decomposable metrics, little is known either about the form of the optimal classifier for a general multi-class non-decomposable metric, or about how these learning algorithms generalize to the multi-class case. In this paper, we provide a unified framework for analysing a multi-class non-decomposable performance metric, where the problem of finding the optimal classifier for the performance metric is viewed as an optimization problem over the space of all confusion matrices achievable under the given distribution. Using this framework, we show that (under a continuous distribution) the optimal classifier for a multi-class performance metric can be obtained as the solution of a

Author

Ramaswamy, Harish G.; Narasimhan, Harikrishna; Agarwal, Shivani

Published

2015

Language

EN