Name
Title | ||
|---|---|---|
NDPMine: efficiently mining discriminative numerical features for pattern-based classification |
Abstract | ||
|---|---|---|
Pattern-based classification has demonstrated its power in recent studies, but because the cost of mining discriminative patterns as features in classification is very expensive, several efficient algorithms have been proposed to rectify this problem. These algorithms assume that feature values of the mined patterns are binary, i.e., a pattern either exists or not. In some problems, however, the number of times a pattern appears is more informative than whether a pattern appears or not. To resolve these deficiencies, we propose a mathematical programming method that directly mines discriminative patterns as numerical features for classification. We also propose a novel search space shrinking technique which addresses the inefficiencies in iterative pattern mining algorithms. Finally, we show that our method is an order of magnitude faster, significantly more memory efficient and more accurate than current approaches. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15883-4_3 | ECML/PKDD |
Keywords | Field | DocType |
pattern-based classification,mines discriminative pattern,mining discriminative pattern,iterative pattern mining algorithm,discriminative numerical feature,mathematical programming method,feature value,current approach,novel search space,efficient algorithm,mined pattern,svm,search space | Pattern recognition,Support vector machine,Discriminative pattern mining,Artificial intelligence,Discriminative model,Machine learning,Mathematics,Binary number | Conference |
Volume | ISSN | ISBN |
6322 | 0302-9743 | 3-642-15882-X |
Citations | PageRank | References |
13 | 0.56 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyungsul Kim | 1 | 186 | 13.18 |
| Sangkyum Kim | 2 | 178 | 10.54 |
| Tim Weninger | 3 | 576 | 46.14 |
| Jiawei Han | 4 | 43085 | 3824.48 |
| Tarek Abdelzaher | 5 | 10179 | 729.36 |