Metric Divergence Measures and Information Value in Credit Scoring
Author(s) -
Guoping Zeng
Publication year - 2013
Publication title -
journal of mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.252
H-Index - 13
eISSN - 2314-4785
pISSN - 2314-4629
DOI - 10.1155/2013/848271
Subject(s) - divergence (linguistics) , metric (unit) , mathematics , class (philosophy) , value (mathematics) , measure (data warehouse) , statistics , artificial intelligence , computer science , data mining , philosophy , linguistics , operations management , economics
Recently, a series of divergence measures have emerged from information theory and statistics and numerous inequalities have been established among them. However, none of them are a metric in topology. In this paper, we propose a class of metric divergence measures, namely, , and study their mathematical properties. We then study an important divergence measure widely used in credit scoring, called information value. In particular, we explore the mathematical reasoning of weight of evidence and suggest a better alternative to weight of evidence. Finally, we propose using as alternatives to information value to overcome its disadvantages
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