Automatically assigned DDC number: 006312

Manually assigned DDC number: 006312

Number of references: 8

Title: Learning with Non-uniform Class and Cost Distributions: Effects and a Distributed Multi-classifier Approach

Author:

Author:

Subject: Philip K. Chan,Salvatore J. Stolfo Learning with Non-uniform Class and Cost Distributions: Effects and a Distributed Multi-classifier Approach

Description: Many factors influence a learning process and the performance of a learned classifier. In this paper we investigate the effects of class distribution in the training set on performance. We also study different methods of measuring performance based on cost models and the performance effects of training class distribution with respect to the different cost models. Observations from these effects help us devise a distributed multiclassifier meta-learning approach to learn in domains with skewed class distributions, non-uniform cost per error, and large amounts of data. One such domain is credit card fraud detection and our empirical results indicate that our approach can significantly reduce loss due to illegitimate transactions. Introduction Inductive learning research has been focusing on devising algorithms that generate highly accurate classifiers. Many factors contribute to the success of a learning process and hence the quality of the learned classifier. One factor is the class d...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 1998-07-30

Pubyear: 1998

Format: ps

Identifier: http://citeseer.ist.psu.edu/191435.html

Source: http://cs.fit.edu/~pkc/papers/ddm98.ps

Language: en

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Rights: unrestricted

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