Name
Abstract | ||
|---|---|---|
This paper addresses an important problem related to the use ofinduction systems in analyzing real world data. The problem is thequality and reliability of the rules generated by the systems.~Wediscuss the significance of having a reliable and efficient rule quality measure. Such a measure can provide useful support ininterpreting, ranking and applying the rules generated by aninduction system. A number of rule quality and statistical measuresare selected from the literature and their performance is evaluatedon four sets of semiconductor data. The primary goal of thistesting and evaluation has been to investigate the performance ofthese quality measures based on: (i) accuracy, (ii) coverage, (iii)positive error ratio, and (iv) negative error ratio of the ruleselected by each measure. Moreover, the sensitivity of these qualitymeasures to different data distributions is examined. Inconclusion, we recommend Cohen‘s statistic as being the best qualitymeasure examined for the domain. Finally, we explain some future workto be done in this area. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1023/A:1008293727412 | Appl. Intell. |
Keywords | DocType | Volume |
intelligent manufacturing,rule quality,machine learning,induction,post-processing | Journal | 7 |
Issue | ISSN | Citations |
2 | 0924-669X | 1 |
PageRank | References | Authors |
0.36 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Dean | 1 | 1 | 0.36 |
| Fazel Famili | 2 | 207 | 18.62 |