Sequence Classification Based on Delta-Free Sequential Pattern

Pierre Holat 1 Marc Plantevit 2 Chedy Raïssi 3 Nadi Tomeh 4 Thierry Charnois 5 Bruno Crémilleux 5
2 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
4 RCLN
LIPN - Laboratoire d'Informatique de Paris-Nord
5 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Sequential pattern mining is one of the most studied and challenging tasks in data mining. However, the extension of well-known methods from many other classical patterns to sequences is not a trivial task. In this paper we study the notion of δ-freeness for sequences. While this notion has extensively been discussed for itemsets, this work is the first to extend it to sequences. We define an efficient algorithm devoted to the extraction of δ-free sequential patterns. Furthermore, we show the advantage of the δ-free sequences and highlight their importance when building sequence classifiers, and we show how they can be used to address the feature selection problem in statistical classifiers, as well as to build symbolic classifiers which optimizes both accuracy and earliness of predictions.
Complete list of metadatas

Cited literature [46 references]  Display  Hide  Download

https://hal.inria.fr/hal-01100929
Contributor : Chedy Raïssi <>
Submitted on : Wednesday, January 7, 2015 - 1:17:48 PM
Last modification on : Tuesday, February 26, 2019 - 6:06:02 PM
Long-term archiving on : Friday, September 11, 2015 - 1:15:37 AM

File

delta-libres.pdf
Files produced by the author(s)

Licence


Copyright

Identifiers

  • HAL Id : hal-01100929, version 1

Citation

Pierre Holat, Marc Plantevit, Chedy Raïssi, Nadi Tomeh, Thierry Charnois, et al.. Sequence Classification Based on Delta-Free Sequential Pattern. IEEE International Conference on Data Mining, Dec 2014, Shenzhen, China. ⟨hal-01100929⟩

Share

Metrics

Record views

1506

Files downloads

743