Classifier evaluation under limited resources

被引：13

作者：

Arbela, Reuven

Rokach, Lior ^{[1
]}

机构：

[1] Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel

[2] Tel Aviv Univ, Dept Ind Engn, IL-69978 Tel Aviv, Israel

来源：

PATTERN RECOGNITION LETTERS | 2006年 / 27卷 / 14期

关键词：

classification; evaluation measures; hit-rate; recall; receiver operating characteristic;

D O I：

10.1016/j.patrec.2006.03.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Existing evaluations measures are insufficient when probabilistic classifiers are used for choosing objects to be included in a limited quota. This paper reviews performance measures that suit probabilistic classification and introduce two novel performance measures that can be used effectively for this task. It then investigates when to use each of the measures and what purpose each one of them serves. The use of these measures is demonstrated on a real life dataset obtained from the human resource field and is validated on set of benchmark datasets. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：1619 / 1631

页数：13

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