Derivations of Normalized Mutual Information in Binary Classifications
| dc.creator | Wang, Yong | |
| dc.creator | Hu, Bao-Gang | |
| dc.date | 2007-11-23 | |
| dc.date.accessioned | 2026-07-07T08:44:37Z | |
| dc.date.available | 2026-07-07T08:44:37Z | |
| dc.description | 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). | |
| dc.description | 8 pages, 8 figures, and 2 tables | |
| dc.identifier | https://arxiv.org/abs/0711.3675 | |
| dc.identifier | http://arxiv.org/abs/0711.3675 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/142723 | |
| dc.subject | Machine Learning | |
| dc.subject | Information Theory | |
| dc.subject | I.5.1 | |
| dc.title | Derivations of Normalized Mutual Information in Binary Classifications | |
| dc.type | text |