Name
Title | ||
|---|---|---|
Reconsideration of the Effectiveness on Extracting Computer Diagnostic Rules by Automatically Defined Groups |
Abstract | ||
|---|---|---|
Our aim is to manage computer systems without expert knowledge. We have proposed a method of diagnostic rule extraction from log files by using Automatically Defined Groups (ADG) based on Genetic Programming. However, this work less explained the effectiveness, especially, the characteristics of the acquired rules. Therefore, we re-evaluated the effectiveness by performing two experiments: the use of artificial log files and the use of real log files. As a result, we confirmed that ADG could acquire the rules composed of multiple terms. This characteristic is very important because we can judge the message that we must consider the co-occurrence of the words, i.e. `Error' and `not'. Thus, we conclude that the ADG is effective for the diagnosis of the systems. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74827-4_52 | KES (2) |
Keywords | Field | DocType |
genetic programming,computer system,diagnostic rule extraction,artificial log file,multiple term,automatically defined groups,acquired rule,extracting computer diagnostic rules,real log file,log file,expert knowledge,data mining | Data mining,Computer science,Genetic programming,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
4693 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoshiaki Kurosawa | 1 | 13 | 5.92 |
| Akira Hara | 2 | 76 | 20.05 |
| Kazuya Mera | 3 | 10 | 6.46 |
| Takumi Ichimura | 4 | 120 | 35.31 |