<div class="csl-bib-body">
<div class="csl-entry">Taghizadeh, G., & Musliu, N. (2017). A Hybrid Feature Selection Algorithm Based on Large Neighborhood Search. In B. Hu & M. López-Ibáñez (Eds.), <i>Evolutionary Computation in Combinatorial Optimization</i> (pp. 30–43). Lecture Notes in Computer Science / Springer. https://doi.org/10.1007/978-3-319-55453-2_3</div>
</div>
-
dc.identifier.isbn
9783319554532
-
dc.identifier.isbn
9783319554525
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/57116
-
dc.description.abstract
Feature selection aims at choosing a small number of relevant features in a data set to achieve similar or even better classification accuracy than using all features. This paper presents the first study on Large Neighborhood Search (LNS) algorithm for the feature selection problem. We propose a novel hybrid Wrapper and Filter feature selection method using LNS algorithm (WFLNS). In LNS, an initial solution is gradually improved by alternately destroying and repairing the solution. We introduce the idea of using filter ranking method in the process of destroying and repairing to accelerate the search in identifying the core feature subsets. Particularly, WFLNS either adds or removes features from a candidate solution based on the correlation based feature ranking method. The proposed algorithm has been tested on twelve benchmark data sets and the results have been compared with ten most recent wrapper methods where WFLNS outperforms other methods in several the data sets.
en
dc.publisher
Lecture Notes in Computer Science / Springer
-
dc.relation.ispartofseries
Lecture Notes in Computer Science
-
dc.subject
Feature selection
-
dc.subject
Large Neighborhood
-
dc.subject
Search Classification
-
dc.title
A Hybrid Feature Selection Algorithm Based on Large Neighborhood Search
-
dc.type
Konferenzbeitrag
de
dc.type
Inproceedings
en
dc.relation.publication
Evolutionary Computation in Combinatorial Optimization
-
dc.relation.isbn
978-3-319-55452-5
-
dc.relation.doi
10.1007/978-3-319-55453-2
-
dc.relation.issn
0302-9743
-
dc.description.startpage
30
-
dc.description.endpage
43
-
dc.type.category
Full-Paper Contribution
-
dc.relation.eissn
1611-3349
-
dc.publisher.place
10197
-
tuw.booktitle
Evolutionary Computation in Combinatorial Optimization
-
tuw.peerreviewed
true
-
tuw.relation.publisher
Springer
-
tuw.relation.publisherplace
Cham
-
tuw.researchTopic.id
I1
-
tuw.researchTopic.name
Logic and Computation
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
tuw.publisher.doi
10.1007/978-3-319-55453-2_3
-
dc.description.numberOfPages
14
-
tuw.event.name
EvoCOP 2017
-
tuw.event.startdate
19-04-2017
-
tuw.event.enddate
21-04-2017
-
tuw.event.online
On Site
-
tuw.event.type
Event for scientific audience
-
tuw.event.place
Amsterdam, Niederlande
-
tuw.event.place
Amsterdam, Niederlande
-
tuw.event.country
EU
-
tuw.event.presenter
Taghizadeh, Gelareh
-
wb.sciencebranch
Informatik
-
wb.sciencebranch.oefos
1020
-
wb.presentation.type
science to science/art to art
-
item.openairecristype
http://purl.org/coar/resource_type/c_5794
-
item.fulltext
no Fulltext
-
item.openairetype
conference paper
-
item.grantfulltext
none
-
item.cerifentitytype
Publications
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence