Conferences in Research and Practice in Information Technology
  

Online Version - Last Updated - 20 Jan 2012

 

 
Home
 

 
Procedures and Resources for Authors

 
Information and Resources for Volume Editors
 

 
Orders and Subscriptions
 

 
Published Articles

 
Upcoming Volumes
 

 
Contact Us
 

 
Useful External Links
 

 
CRPIT Site Search
 
    

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.
pdf (from crpit.com) pdf (local if available) BibTeX EndNote GS
 

 

ACS Logo© Copyright Australian Computer Society Inc. 2001-2014.
Comments should be sent to the webmaster at crpit@scem.uws.edu.au.
This page last updated 16 Nov 2007