Memory-Assisted Dynamic Multi-Objective Evolutionary Algorithm for Feature Drift Problem

In this paper, we propose an enhanced feature selection algorithm able to cope with feature drift problem that may occur in data streams, where the set of relevant features change over time. We utilize a dynamic multi-objective evolutionary algorithm to continuously search for the updated set of rel...

Full description

Saved in:
Bibliographic Details
Published in:2020 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors: Sahmoud, Shaaban, Topcuoglu, Haluk Rahmi
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2020
Subjects:
dynamic multi-objective evolutionary algorithms
Evolutionary computation
feature drift
Feature extraction
Heuristic algorithms
learning in non-stationary environments
memory-based algorithms
Optimization
Power system dynamics
severity of changes
Sociology
Statistics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract In this paper, we propose an enhanced feature selection algorithm able to cope with feature drift problem that may occur in data streams, where the set of relevant features change over time. We utilize a dynamic multi-objective evolutionary algorithm to continuously search for the updated set of relevant features after the occurrence of every change in the environment. An artificial neural network is employed to classify the new instances based on the up-to-date obtained set of relevant features efficiently. Our algorithm exploits a detection mechanism for the severity of changes to estimate the severity level of occurred changes and adaptively replies to these changes by introducing diversity to algorithm solutions. Furthermore, a fixed-size memory is used to store the good solutions and reuse them after each change to accelerate the convergence and searching process of the algorithm. The experimental results using three datasets and different environmental parameters show that the combination of our improved feature selection algorithm with the artificial neural network outperforms related work.
AbstractList In this paper, we propose an enhanced feature selection algorithm able to cope with feature drift problem that may occur in data streams, where the set of relevant features change over time. We utilize a dynamic multi-objective evolutionary algorithm to continuously search for the updated set of relevant features after the occurrence of every change in the environment. An artificial neural network is employed to classify the new instances based on the up-to-date obtained set of relevant features efficiently. Our algorithm exploits a detection mechanism for the severity of changes to estimate the severity level of occurred changes and adaptively replies to these changes by introducing diversity to algorithm solutions. Furthermore, a fixed-size memory is used to store the good solutions and reuse them after each change to accelerate the convergence and searching process of the algorithm. The experimental results using three datasets and different environmental parameters show that the combination of our improved feature selection algorithm with the artificial neural network outperforms related work.
Author Topcuoglu, Haluk Rahmi
Sahmoud, Shaaban
Author_xml – sequence: 1
  givenname: Shaaban
  surname: Sahmoud
  fullname: Sahmoud, Shaaban
  organization: Fatih Sultan Mehmet Vakif University,Computer Engineering Department,Istanbul,Turkey
– sequence: 2
  givenname: Haluk Rahmi
  surname: Topcuoglu
  fullname: Topcuoglu, Haluk Rahmi
  organization: Marmara University,Computer Engineering Department,Istanbul,Turkey
BookMark eNotj09LwzAcQCPowU0_gQj5Aq3506TJsXSdEzbmQcHbSJtfNNI2kqaDfnsFd3q3x3srdD2GERB6pCSnlOinuqkLJYnMGWEk11SJkpMrtKIlU1Rqpukt-jjAEOKSVdPkpwQWb5bRDL7Dh7lPPju239AlfwbcnEM_Jx9GExdc9Z8h-vQ1YBci3oJJcwS8id4l_BpD28Nwh26c6Se4v3CN3rfNW73L9sfnl7raZ55SlTIhtS6IYl1nmVPcSlFoy4QDZwRvTcuc7ig3JVHCcW07KUmplSmNFaAUKL5GD_9eDwCnn-iHv8DTZZb_AnvjT-w
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/CEC48606.2020.9185730
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Statistics
EISBN 1728169291
9781728169293
EndPage 8
ExternalDocumentID 9185730
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i118t-56994082ccd2f83d6549d25fefa53bab2f9c13a7085f39dc660798a7ad5e88e83
IEDL.DBID RIE
IngestDate Mon Jul 08 05:38:35 EDT 2024
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-56994082ccd2f83d6549d25fefa53bab2f9c13a7085f39dc660798a7ad5e88e83
PageCount 8
ParticipantIDs ieee_primary_9185730
PublicationCentury 2000
PublicationDate 2020-July
PublicationDateYYYYMMDD 2020-07-01
PublicationDate_xml – month: 07
  year: 2020
  text: 2020-July
PublicationDecade 2020
PublicationTitle 2020 IEEE Congress on Evolutionary Computation (CEC)
PublicationTitleAbbrev CEC
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.7864611
Snippet In this paper, we propose an enhanced feature selection algorithm able to cope with feature drift problem that may occur in data streams, where the set of...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms dynamic multi-objective evolutionary algorithms
Evolutionary computation
feature drift
Feature extraction
Heuristic algorithms
learning in non-stationary environments
memory-based algorithms
Optimization
Power system dynamics
severity of changes
Sociology
Statistics
Title Memory-Assisted Dynamic Multi-Objective Evolutionary Algorithm for Feature Drift Problem
URI https://ieeexplore.ieee.org/document/9185730
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELVKxdCJjxbxLQ-MuE3ixLFH1A8xQOkAqFvl2Gcooi2K0kr99_iSqAiJhS2KElm6s-z37Hv3CLnhNuQ2AmCxCGIWh34aZ05Z5qTyIAlM4MpOTK8P6Xgsp1M1aZDbnRYGAMriM-jiY3mXb1dmjUdlPYWNi7gn6HtpKiqtVi3KCQPV6w_76KiEdQdR0K2__WWaUu4Zo4P_jXZIOj_iOzrZbStHpAHLY9JCUFj1VG6T6SOWx26ZDy0mydJBZStPSzUte8o-qlWMDjf1xNL5lt59vq3yefG-oB6nUoR-6xzoIJ-7AsdDW5kOeRkNn_v3rHZIYHNPDAqWCKXQMdoYGznJrfBsz0aJA6cTnukscsqEXKceVzmurBEiSJXUqbYJSAmSn5DmcrWEU0Jj_zO3Hj5JT_hSo3UopOdm2igthY7hjLQxRLOvqgnGrI7O-d-vL0gLs1DVtV6SZpGv4Yrsm40PVX5dZu4b0kuciA
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NTwIxEJ0QNJGTH2D8tgePFna3-9EeDWAwAnJAw42UdqoYBbNZSPj3trsbjIkXb5vNbprMNO177bx5ADdM-0wHiDSMvZCGvp3GMyM0NVxYkITKM3knppd-MhzyyUSMKnC71cIgYl58hk33mN_l66VauaOylnCNi5gl6DtRGAZeodYqZTm-J1rtbtt5KrnKg8Brll__sk3Jd437_f-NdwCNH_kdGW03lkOo4OIIag4WFl2V6zAZuALZDbXBdWnSpFMYy5NcT0ufZu_FOka663JqyXRD7j5el-k8e_skFqkSB_5WKZJOOjeZG88ZyzTg-b47bvdo6ZFA55YaZDSKhXCe0UrpwHCmY8v3dBAZNDJiMzkLjFA-k4lFVoYJreLYSwSXidQRco6cHUN1sVzgCZDQ_sy0BVDcUr5ESenH3LIzqYTksQzxFOouRNOvog3GtIzO2d-vr2GvNx70p_2H4eM51FxGiirXC6hm6QovYVetbdjSqzyL3yQmn88
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2020+IEEE+Congress+on+Evolutionary+Computation+%28CEC%29&rft.atitle=Memory-Assisted+Dynamic+Multi-Objective+Evolutionary+Algorithm+for+Feature+Drift+Problem&rft.au=Sahmoud%2C+Shaaban&rft.au=Topcuoglu%2C+Haluk+Rahmi&rft.date=2020-07-01&rft.pub=IEEE&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1109%2FCEC48606.2020.9185730&rft.externalDocID=9185730