Evaluation multi label feature selection for text classification using weighted borda count approach

Due to the existence of text data, multi-label (ML) text classification is an essential task in machine learning. Feature selection is an essential and effective preprocess to enhance the learning process. Choosing a Multi-Label Feature Selection (MLFS) algorithm is the most basic, critical, and sen...

Full description

Saved in:
Bibliographic Details
Published in:Iranian Joint Congress on Fuzzy and Intelligent Systems (Online) pp. 1 - 6
Main Authors: Miri, Mohsen, Dowlatshahi, Mohammad Bagher, Hashemi, Amin
Format: Conference Proceeding
Language:English
Published: IEEE 02.03.2022
Subjects:
Classification algorithms
Feature extraction
Machine learning
Machine learning algorithms
Multi-label feature selection
Task analysis
Text categorization
Text classification
Voting
Weighted Borda Count
ISSN:2771-1374
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Due to the existence of text data, multi-label (ML) text classification is an essential task in machine learning. Feature selection is an essential and effective preprocess to enhance the learning process. Choosing a Multi-Label Feature Selection (MLFS) algorithm is the most basic, critical, and sensitive choice in ML classification operations. If this choice is based on a criterion, it cannot be attributed to always being sound. Choosing the best algorithm must be evaluated using several different criteria to be examined from different aspects. In this article, we turn the issue into an election and use the Weighted Borda Count method for voting. We do the voting in three stages continuously so that a subset of different features does the voting. In the second stage, voting of different methods is done with six criteria, and each criterion selects the methods in order of priority from the beginning to the end. Voting steps 1 and 2 are performed on eighteen text datasets used. Finally, in the final voting stage, the methods are evaluated and voted on by different text datasets. The final result of the voting in the third stage shows the desired MLFS methods based on their performance from beginning to end. According to the experiments performed and the results obtained, it can be seen that the selection of the algorithm based on several different criteria and considering the overall performance of the algorithm will be better than the selection based on one criterion.
AbstractList Due to the existence of text data, multi-label (ML) text classification is an essential task in machine learning. Feature selection is an essential and effective preprocess to enhance the learning process. Choosing a Multi-Label Feature Selection (MLFS) algorithm is the most basic, critical, and sensitive choice in ML classification operations. If this choice is based on a criterion, it cannot be attributed to always being sound. Choosing the best algorithm must be evaluated using several different criteria to be examined from different aspects. In this article, we turn the issue into an election and use the Weighted Borda Count method for voting. We do the voting in three stages continuously so that a subset of different features does the voting. In the second stage, voting of different methods is done with six criteria, and each criterion selects the methods in order of priority from the beginning to the end. Voting steps 1 and 2 are performed on eighteen text datasets used. Finally, in the final voting stage, the methods are evaluated and voted on by different text datasets. The final result of the voting in the third stage shows the desired MLFS methods based on their performance from beginning to end. According to the experiments performed and the results obtained, it can be seen that the selection of the algorithm based on several different criteria and considering the overall performance of the algorithm will be better than the selection based on one criterion.
Author Hashemi, Amin
Miri, Mohsen
Dowlatshahi, Mohammad Bagher
Author_xml – sequence: 1
  givenname: Mohsen
  surname: Miri
  fullname: Miri, Mohsen
  email: miri.mo@fe.lu.ac.ir
  organization: Lorestan University,Faculty of Engineering,Department of Computer Engineering,Khorramabad,Iran
– sequence: 2
  givenname: Mohammad Bagher
  surname: Dowlatshahi
  fullname: Dowlatshahi, Mohammad Bagher
  email: dowlatshahi.mb@lu.ac.ir
  organization: Lorestan University,Faculty of Engineering,Department of Computer Engineering,Khorramabad,Iran
– sequence: 3
  givenname: Amin
  surname: Hashemi
  fullname: Hashemi, Amin
  email: hashemi.am@fe.lu.ac.ir
  organization: Lorestan University,Faculty of Engineering,Department of Computer Engineering,Khorramabad,Iran
BookMark eNotkMtKAzEYhaMo2NY-gSB5gRmTTG6zlNJqoeBCXZd_Mn_aSDpTJhkvb6_Yrg6cj_MtzpRcdX2HhNxzVnLO6ofFav2qpDGyFEyIsjZKS20uyJRr_ddbI_glmQhjeMErI2_IPKUPxlglmGRCTUi7_IQ4Qg59Rw9jzIFGaDBSj5DHAWnCiO6f-n6gGb8zdRFSCj6402pModvRLwy7fcaWNv3QAnX92GUKx-PQg9vfkmsPMeH8nDPyvlq-LZ6LzcvTevG4KQLnNhfWK4mmUaiFakQrtTAordLKV8xqz6AGLR0HXzWKN7bmEkRjlFOibaVStpqRu5M3IOL2OIQDDD_b8ynVLwLsWiE
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/CFIS54774.2022.9756467
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
EISBN 1665478721
9781665478724
EISSN 2771-1374
EndPage 6
ExternalDocumentID 9756467
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i118t-8f54e7b5e625b2d4627e48565f3086f0a9a64c1af3b51b8914a2b75c52dd45583
IEDL.DBID RIE
IngestDate Wed Aug 27 02:24:19 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-8f54e7b5e625b2d4627e48565f3086f0a9a64c1af3b51b8914a2b75c52dd45583
PageCount 6
ParticipantIDs ieee_primary_9756467
PublicationCentury 2000
PublicationDate 2022-March-2
PublicationDateYYYYMMDD 2022-03-02
PublicationDate_xml – month: 03
  year: 2022
  text: 2022-March-2
  day: 02
PublicationDecade 2020
PublicationTitle Iranian Joint Congress on Fuzzy and Intelligent Systems (Online)
PublicationTitleAbbrev CFIS
PublicationYear 2022
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0003204025
Score 1.8288891
Snippet Due to the existence of text data, multi-label (ML) text classification is an essential task in machine learning. Feature selection is an essential and...
SourceID ieee
SourceType Publisher
StartPage 1
SubjectTerms Classification algorithms
Feature extraction
Machine learning
Machine learning algorithms
Multi-label feature selection
Task analysis
Text categorization
Text classification
Voting
Weighted Borda Count
Title Evaluation multi label feature selection for text classification using weighted borda count approach
URI https://ieeexplore.ieee.org/document/9756467
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA5t8eBJpRXf5ODR1G422SRnaVGQUlCht5LHRArSSh_6952k64rgxVvYMCzMN8s89psZQq4llOjWrGDRgGMiFoGZEgomveAqenQINiP9qMZjPZ2aSYvcNL0wAJDJZ9BPx_wvPyz9NpXKbo2SFX7YbdJWqtr1ajX1lJKjOXJZNwEXA4NW8_AkBYY3mAVy3q-Ff21RyU5kdPC_1x-S3k83Hp00fuaItGDRJWHYjOmmmRRIEU54oxHypE66zvtt0i1GpTTRO6hPgXJiBu2kEuP9lX7m0igEirYQLM2rI-j3oPEeeRkNn-_uWb0xgc0xUdgwHaUA5SRgVuN4EBVXIDTGbLHE1CUOrLGV8IWNpZOF0waR4E5JL3kIQkpdHpPOYrmAE0Jt0CJq5YzhIFC5GlzkZfRWeTeIUJ2SbtLQ7H03FGNWK-fs78fnZD-BkMlb_IJ0NqstXJI9_7GZr1dXGckvPSShqg
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEA61CnpSacW3OXh062422SRnaWmxloIVeiubZCIFaaUP_ftO0roiePEWNgws880yj_1mhpBbATm6tZInXoNJuM9conPIEmE5k96iQygj0n05GKjxWA9r5K7qhQGASD6DVjjGf_lubtehVHavpSjww94hu4Jzlm66taqKSs7QIJnYtgFnqUa76T0LjgEO5oGMtbbiv_aoRDfSOfzfCxyR5k8_Hh1WnuaY1GDWIK5dDeqmkRZIEVB4ox7irE66jBtuwi3GpTQQPKgNoXLgBm2kAuf9lX7G4ig4itbgShqXR9DvUeNN8tJpjx66yXZnQjLFVGGVKC84SCMA8xrDHC-YBK4wavM5Ji8-LXVZcJuVPjciM0ojFsxIYQVzjguh8hNSn81ncEpo6RT3ShqtGXBUrgLjWe5tKa1JPRRnpBE0NHnfjMWYbJVz_vfjG7LfHT31J_3e4PGCHARAIpWLXZL6arGGK7JnP1bT5eI6ovoF0nik8Q
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%3Ajournal&rft.genre=proceeding&rft.title=Iranian+Joint+Congress+on+Fuzzy+and+Intelligent+Systems+%28Online%29&rft.atitle=Evaluation+multi+label+feature+selection+for+text+classification+using+weighted+borda+count+approach&rft.au=Miri%2C+Mohsen&rft.au=Dowlatshahi%2C+Mohammad+Bagher&rft.au=Hashemi%2C+Amin&rft.date=2022-03-02&rft.pub=IEEE&rft.eissn=2771-1374&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FCFIS54774.2022.9756467&rft.externalDocID=9756467