University of Hertfordshire

By the same authors

Selecting Features in Origin Analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Original languageEnglish
Title of host publicationResearch and Development in Intelligent Systems XXVII, Incorporating Applications and Innovations in Intelligent Systems XVIII,
Subtitle of host publicationProceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
PublisherSpringer
Pages379-392
ISBN (Electronic)978-0-85729-130-1
ISBN (Print)978-0-85729-129-5
Publication statusPublished - 2010

Abstract

When applying a machine-learning approach to develop classifiers in a new domain, an important question is what measurements to take and how they will be used to construct informative features. This paper develops a novel set of machine-learning classifiers for the domain of classifying files taken from software projects; the target classifications are based on origin analysis. Our approach adapts the output of four copy-analysis tools, generating a number of different measurements. By combining the measures and the files on which they operate, a large set of features is generated in a semi-automatic manner. After which, standard attribute selection and classifier training techniques yield a pool of high quality classifiers (accuracy in the range of 90%), and information on the most relevant features.

Notes

Original paper can be found at: http://www.springer.com/computer/ai/book/978-0-85729-129-5 Copyright Springer

Research outputs

ID: 98385