Abstract
A common practice when filtering a case-base is to employ a filtering scheme that decides which cases to delete, as well as how many cases to delete, such that the storage requirements are minimized and the classification competence is preserved or improved. We introduce an algorithm that rivals the most successful existing algorithm in the average case when filtering 30 classification problems. Neither algorithm consistently outperforms the other, with each performing well on different problems. Consistency over many domains, we argue, is very hard to achieve when deploying a filtering algorithm.
| Original language | English |
|---|---|
| Title of host publication | PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY |
| Editors | JM Zytkow, J Rauch |
| Publisher | SPRINGER-VERLAG BERLIN |
| Pages | 283-288 |
| Number of pages | 6 |
| ISBN (Print) | 3540664904 |
| Publication status | Published - 1999 |
| Externally published | Yes |
| Event | 3rd European Conference on Principles of Data Mining and Knowledge Discovery in Databases (PKDD 99) - PRAGUE, Czech Republic Duration: 15 Sep 1999 → 18 Sep 1999 |
Publication series
| Name | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE |
|---|---|
| Publisher | SPRINGER-VERLAG BERLIN |
| Volume | 1704 |
| ISSN (Print) | 0302-9743 |
Conference
| Conference | 3rd European Conference on Principles of Data Mining and Knowledge Discovery in Databases (PKDD 99) |
|---|---|
| Country | Czech Republic |
| City | PRAGUE |
| Period | 15/09/99 → 18/09/99 |
Cite this
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On the consistency of information filters for lazy learning algorithms. / Brighton, H; Mellish, C.
PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY. ed. / JM Zytkow; J Rauch. SPRINGER-VERLAG BERLIN, 1999. p. 283-288 (LECTURE NOTES IN ARTIFICIAL INTELLIGENCE; Vol. 1704).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
TY - GEN
T1 - On the consistency of information filters for lazy learning algorithms
AU - Brighton, H
AU - Mellish, C
PY - 1999
Y1 - 1999
N2 - A common practice when filtering a case-base is to employ a filtering scheme that decides which cases to delete, as well as how many cases to delete, such that the storage requirements are minimized and the classification competence is preserved or improved. We introduce an algorithm that rivals the most successful existing algorithm in the average case when filtering 30 classification problems. Neither algorithm consistently outperforms the other, with each performing well on different problems. Consistency over many domains, we argue, is very hard to achieve when deploying a filtering algorithm.
AB - A common practice when filtering a case-base is to employ a filtering scheme that decides which cases to delete, as well as how many cases to delete, such that the storage requirements are minimized and the classification competence is preserved or improved. We introduce an algorithm that rivals the most successful existing algorithm in the average case when filtering 30 classification problems. Neither algorithm consistently outperforms the other, with each performing well on different problems. Consistency over many domains, we argue, is very hard to achieve when deploying a filtering algorithm.
M3 - Conference contribution
SN - 3540664904
T3 - LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
SP - 283
EP - 288
BT - PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY
A2 - Zytkow, JM
A2 - Rauch, J
PB - SPRINGER-VERLAG BERLIN
ER -