Name
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. |
Year | Venue | Keywords |
|---|---|---|
2004 | ACSW Frontiers | data mining,early assessment,classification model,processed information,pre-specified class,smaller sample size,wide attention,learning curve.,automatic extraction,intelligent behavior,classification,different class,accuracy,assessment,classification performance,learning curve,sample size |
Field | DocType | Citations |
Data mining,One-class classification,Computer science,Correctness,Sample size determination | Conference | 5 |
PageRank | References | Authors |
0.56 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bostjan Brumen | 1 | 260 | 25.48 |
| Izidor Golob | 2 | 24 | 3.89 |
| Hannu Jaakkola | 3 | 302 | 60.55 |
| Tatjana Welzer | 4 | 219 | 120.18 |
| Ivan Rozman | 5 | 414 | 122.20 |