Early assessment of classification performance

Authors:
Boštjan Brumen;Izidor Golob;Hannu Jaakkola;Tatjana Welzer;Ivan Rozman
Affiliations:
University of Maribor, Maribor, Slovenia;University of Maribor, Maribor, Slovenia;Tampere University of Technology, Pohjoisranta, Pori, Finland;University of Maribor, Maribor, Slovenia;University of Maribor, Maribor, Slovenia
Venue:
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Year:
2004

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Abstract

The ability to distinguish between objects is the fundamental to learning and intelligent behavior in general. The difference between two things is the information we seek; the processed information is actually the base for the knowledge. Automatic extraction of knowledge has been in interest ever since the advent of computing, and has received a wide attention with the successes of data mining. One of the tasks of data mining is also classification, which provides a mapping from attributes (observations) to pre-specified classes. Based on the distinction between the objects they are mapped into different classes.In the paper, we present an approach for early assessment of the extracted knowledge (classification models) in the terms of performance (accuracy). The assessment is based on the observation of the performance on smaller sample sizes. The solution is formally defined and used in an experiment. The results confirm the correctness of the approach.