Early Assessment of Classification Performance

Brumen, B., Golob, I., Jaakkola, H., Welzer, T. and Rozman, I.

    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.
Cite as: Brumen, B., Golob, I., Jaakkola, H., Welzer, T. and Rozman, I. (2004). Early Assessment of Classification Performance. In Proc. Australasian Workshop on Data Mining and Web Intelligence (DMWI2004), Dunedin, New Zealand. CRPIT, 32. Purvis, M., Ed. ACS. 91-96.
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