Paper: Fu B , Liu H , Jiang Z , et al. D-FS: A Novel Integration Method of Discretization and Feature Selection[C]// International Conference on International Symposium on Pervasive Systems. IEEE Computer Society, 2017.
Abstract: Discretization and feature selection are two basic preprocessing stages of data mining. However, it often results in information loss due to these two separate stages. This paper proposes a novel supervised multivariate discretizer integrated with feature selection, called D-FS. It takes into consideration of the interactions of both different cut-points and features, and achieves feature selection by discretization. D-FS can avoid the information loss caused by the independence of discretization and feature selection. Compared with several state-of-the-art discretizers, D-FS retains a smaller subset of both cut-points and features, while achieves competitive classification performance combined with different classifiers.
Running Requirements:
- jdk 1.7+
- KEEL.jar
- weka.jar
libs can be downloaded freely from internet. The source code is a demo used for academic exchange!
Bibtex:
@inproceedings{DBLP:conf/ispan/FuLJWH17,
author = {Bin Fu and
Hongzhi Liu and
Zhengshen Jiang and
Zhonghai Wu and
D. Frank Hsu},
title = {{D-FS:} {A} Novel Integration Method of Discretization and Feature
Selection},
booktitle = {14th International Symposium on Pervasive Systems, Algorithms and
Networks {\&} 11th International Conference on Frontier of Computer
Science and Technology {\&} Third International Symposium of Creative
Computing, {ISPAN-FCST-ISCC} 2017, Exeter, United Kingdom, June 21-23,
2017},
pages = {6--13},
year = {2017},
crossref = {DBLP:conf/ispan/2017},
url = {https://doi.org/10.1109/ISPAN-FCST-ISCC.2017.64},
doi = {10.1109/ISPAN-FCST-ISCC.2017.64},
timestamp = {Mon, 11 Dec 2017 14:05:41 +0100},
biburl = {https://dblp.org/rec/bib/conf/ispan/FuLJWH17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}