Name
Abstract | ||
|---|---|---|
While a number of efficient sequential pattern mining algorithms were developed over the years, they can still take a long time and produce a huge number of patterns, many of which are redundant. These properties are especially frustrating when the goal of pattern mining is to find patterns for use as features in classification problems. In this paper, we describe BIDE-Discriminative, a modification of BIDE that uses class information for direct mining of predictive sequential patterns. We then perform an extensive evaluation on nine real-life datasets of the different ways in which the basic BIDE-Discriminative can be used in real multi-class classification problems, including 1-versus-rest and model-based search tree approaches. The results of our experiments show that 1-versus-rest provides an efficient solution with good classification performance. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s10115-014-0817-0 | Knowledge and Information Systems |
Keywords | Field | DocType |
Sequential pattern mining, Sequence classification, Information gain | Data mining,Computer science,Information gain,Artificial intelligence,Sequential Pattern Mining,Machine learning,Search tree | Journal |
Volume | Issue | ISSN |
45 | 3 | 0219-3116 |
Citations | PageRank | References |
25 | 0.81 | 44 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitriy Fradkin | 1 | 344 | 19.25 |
| Fabian Mörchen | 2 | 372 | 17.94 |