Derivations of Normalized Mutual Information in Binary Classifications

Details Derivations of Normalized Mutual Information in Binary Classifications Derivations of Normalized Mutual Information in Binary Classifications

2007-11-23

arxiv.org/abs/0711.3675v1

Computer Science

Learning

8 pages, 8 figures, and 2 tables

Scientific paper

This correspondence studies the basic problem of classifications - how to evaluate different classifiers. Although the conventional performance indexes, such as accuracy, are commonly used in classifier selection or evaluation, information-based criteria, such as mutual information, are becoming popular in feature/model selections. In this work, we propose to assess classifiers in terms of normalized mutual information (NI), which is novel and well defined in a compact range for classifier evaluation. We derive close-form relations of normalized mutual information with respect to accuracy, precision, and recall in binary classifications. By exploring the relations among them, we reveal that NI is actually a set of nonlinear functions, with a concordant power-exponent form, to each performance index. The relations can also be expressed with respect to precision and recall, or to false alarm and hitting rate (recall).

Affiliated with

Also associated with

No associations

Rating

Derivations of Normalized Mutual Information in Binary Classifications does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Derivations of Normalized Mutual Information in Binary Classifications, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Derivations of Normalized Mutual Information in Binary Classifications will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-363160