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.
| Original language | English |
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| Title of host publication | Research and Development in Intelligent Systems XXVII, Incorporating Applications and Innovations in Intelligent Systems XVIII, |
| Subtitle of host publication | Proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence |
| Publisher | Springer Nature |
| Pages | 379-392 |
| ISBN (Electronic) | 978-0-85729-130-1 |
| ISBN (Print) | 978-0-85729-129-5 |
| Publication status | Published - 2010 |
Keywords
- data mining
- feature construction
- origin analysis
- machine learning