Name
Abstract | ||
|---|---|---|
The main objective of this paper is to compare the classification accuracy provided by large, comprehensive collections of patterns (rules) derived from archives of past observations, with that provided by small, comprehensible collections of patterns. This comparison is carried out here on the basis of an empirical study, using several publicly available data sets. The results of this study show that the use of comprehensive collections allows a slight increase of classification accuracy, and that the ''cost of comprehensibility'' is small. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.dam.2005.02.035 | Discrete Applied Mathematics |
Keywords | Field | DocType |
prime pattern,slight increase,spanned pattern,comprehensive collection,classification accuracy,pattern-based classifier,empirical study,main objective,logical analysis of data (lad),past observation,available data set,pattern,study show,comprehensible collection,logical analysis | Data set,Computer science,Logical analysis of data,Artificial intelligence,Classifier (linguistics),Empirical research,Machine learning | Journal |
Volume | Issue | ISSN |
156 | 6 | Discrete Applied Mathematics |
Citations | PageRank | References |
18 | 1.02 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriela Alexe | 1 | 197 | 12.75 |
| sorin alexe | 2 | 169 | 10.56 |
| Peter L. Hammer | 3 | 1996 | 288.93 |
| Alexander Kogan | 4 | 260 | 18.78 |