Evaluation of Classifiers for an Uneven Class Distribution Problem

Classification problems with uneven class distributions present several difficulties during the training as well as during the evaluation process of classifiers. A classification problem with such characteristics has resulted from a data-mining project where the objective was to predict customer insolvency. Using the dataset from the customer insolvency problem we study several alternative methodologies which have been reported to better suit the specific characteristics of this type of problems. Three different but equally important directions are examined; (a) the performance measures that should be used for problems in this domain, (b) the class distributions that should be used for the training data sets, (c) the classification algorithms to be used. The final evaluation of the resulting classifiers is based on a study of the economic impact of classification results. This study concludes to a framework that provides the "best" classifiers, identifies the performance mea...

• Type: Journal
• Year: 2006
• Where: AAI
• Authors: Sophia Daskalaki, Ioannis Kopanas, Nikolaos M. Avouris
• Keywords: AAI 2006, Classification Problem, Customer Insolvency, Uneven Class Distributions