Rank Aggregation Based Text Feature Selection
Filtering feature selection method (filtering method, for short) is a well-known feature selection strategy in pattern recognition and data mining. Filtering method outperforms other feature selection methods in many cases when the dimension of features is large. There are so many filtering methods...
Gespeichert in:
| Veröffentlicht in: | Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 Jg. 1; S. 165 - 172 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
Washington, DC, USA
IEEE Computer Society
15.09.2009
IEEE |
| Schriftenreihe: | ACM Conferences |
| Schlagworte: |
Computing methodologies
> Modeling and simulation
> Model development and analysis
> Model verification and validation
Computing methodologies
> Modeling and simulation
> Model development and analysis
> Modeling methodologies
Mathematics of computing
> Probability and statistics
> Probabilistic representations
> Markov networks
|
| ISBN: | 0769538010, 9780769538013 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Filtering feature selection method (filtering method, for short) is a well-known feature selection strategy in pattern recognition and data mining. Filtering method outperforms other feature selection methods in many cases when the dimension of features is large. There are so many filtering methods proposed in previous work leading to the “selection trouble” that how to select an appropriate filtering method for a given text data set. Since to find the best filtering method is usually intractable in real application, this paper takes an alternative path. We propose a feature selection framework that fuses the results obtained by different filtering methods. In fact, deriving a better rank list from different rank lists, known as rank aggregation, is a hot topic studied in many disciplines. Based on the proposed framework and Markov chains rank aggregation techniques, in this paper, we present two new feature selection methods: FR-MC1 and FR-MC4. We also introduce a perturbation algorithm to alleviate the drawbacks of Markov chains rank aggregation techniques. Empirical evaluation on two public text data sets shows that the two new feature selection methods achieve better or comparable results than classical filtering methods, which also demonstrate the effectiveness of our framework. |
|---|---|
| AbstractList | Filtering feature selection method (filtering method, for short) is a well-known feature selection strategy in pattern recognition and data mining. Filtering method outperforms other feature selection methods in many cases when the dimension of features is large. There are so many filtering methods proposed in previous work leading to the “selection trouble” that how to select an appropriate filtering method for a given text data set. Since to find the best filtering method is usually intractable in real application, this paper takes an alternative path. We propose a feature selection framework that fuses the results obtained by different filtering methods. In fact, deriving a better rank list from different rank lists, known as rank aggregation, is a hot topic studied in many disciplines. Based on the proposed framework and Markov chains rank aggregation techniques, in this paper, we present two new feature selection methods: FR-MC1 and FR-MC4. We also introduce a perturbation algorithm to alleviate the drawbacks of Markov chains rank aggregation techniques. Empirical evaluation on two public text data sets shows that the two new feature selection methods achieve better or comparable results than classical filtering methods, which also demonstrate the effectiveness of our framework. |
| Author | Zhu, Mingliang Hu, Weiming Wu, Ou Wang, Hanzi Zuo, Haiqiang Gao, Jun |
| Author_xml | – sequence: 1 givenname: Ou surname: Wu fullname: Wu, Ou – sequence: 2 givenname: Haiqiang surname: Zuo fullname: Zuo, Haiqiang – sequence: 3 givenname: Mingliang surname: Zhu fullname: Zhu, Mingliang – sequence: 4 givenname: Weiming surname: Hu fullname: Hu, Weiming – sequence: 5 givenname: Jun surname: Gao fullname: Gao, Jun – sequence: 6 givenname: Hanzi surname: Wang fullname: Wang, Hanzi |
| BookMark | eNqNkL1OwzAURo0ACVq6srBkY0q513-xx1JRiFQJCYI6Wk58XYW2CUqCBG9PS3kApm84R99wRuysaRti7Bphigj2bpWn-ayYcgA7FfyETWxmUHIplRAoTtkIMm2VMIBwwSZ9_w4AiByk0pcsffHNJpmt1x2t_VC3TXLvewpJQV9DsiA_fHaUvNKWqgO8YufRb3ua_O2YvS0eivlTunx-zOezZepRw5BWoCPyrBTBBjQkgwEvDWoKmspQVVBKblEIHrSusr1tYihVUDFGq7LMijG7Of7WROQ-unrnu2-nuNHwS2-P1Fc7V7btpncI7tDCrXK3b-EOLZzgezP9n-nKrqYofgDlfl44 |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/WI-IAT.2009.32 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume 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 | Computer Science |
| EISBN | 9781424453313 1424453313 |
| EndPage | 172 |
| ExternalDocumentID | 5286079 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IK 6IL 6IN AAJGR AARBI ACM ADPZR ALMA_UNASSIGNED_HOLDINGS APO BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK GUFHI IERZE OCL RIB RIC RIE RIL AAWTH LHSKQ |
| ID | FETCH-LOGICAL-a160t-c06f127b3d9d18e4d80a4816ed6ebdcc0b4291332d66c7c068fdb5d5fff957793 |
| IEDL.DBID | RIE |
| ISBN | 0769538010 9780769538013 |
| IngestDate | Wed Aug 27 01:35:30 EDT 2025 Wed Jan 31 06:43:04 EST 2024 Wed Jan 31 06:46:29 EST 2024 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Keywords | Rank aggregation Text feature selection Markov chains |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a160t-c06f127b3d9d18e4d80a4816ed6ebdcc0b4291332d66c7c068fdb5d5fff957793 |
| PageCount | 8 |
| ParticipantIDs | acm_books_10_1109_WI_IAT_2009_32 ieee_primary_5286079 acm_books_10_1109_WI_IAT_2009_32_brief |
| PublicationCentury | 2000 |
| PublicationDate | 20090915 2009-Sept. |
| PublicationDateYYYYMMDD | 2009-09-15 2009-09-01 |
| PublicationDate_xml | – month: 09 year: 2009 text: 20090915 day: 15 |
| PublicationDecade | 2000 |
| PublicationPlace | Washington, DC, USA |
| PublicationPlace_xml | – name: Washington, DC, USA |
| PublicationSeriesTitle | ACM Conferences |
| PublicationTitle | Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 |
| PublicationTitleAbbrev | WIIAT |
| PublicationYear | 2009 |
| Publisher | IEEE Computer Society IEEE |
| Publisher_xml | – name: IEEE Computer Society – name: IEEE |
| SSID | ssj0001120456 |
| Score | 1.4764655 |
| Snippet | Filtering feature selection method (filtering method, for short) is a well-known feature selection strategy in pattern recognition and data mining. Filtering... |
| SourceID | ieee acm |
| SourceType | Publisher |
| StartPage | 165 |
| SubjectTerms | Applied computing -- Document management and text processing Automation Computer science Computing methodologies -- Machine learning -- Machine learning algorithms -- Feature selection Computing methodologies -- Modeling and simulation -- Model development and analysis Computing methodologies -- Modeling and simulation -- Model development and analysis -- Model verification and validation Computing methodologies -- Modeling and simulation -- Model development and analysis -- Modeling methodologies Conferences Content addressable storage Data mining Fuses Information filtering Information filters Information systems -- Information retrieval -- Retrieval models and ranking Information systems -- Information retrieval -- Retrieval tasks and goals Intelligent agent Markov chains Mathematics of computing -- Probability and statistics -- Probabilistic representations -- Markov networks Mathematics of computing -- Probability and statistics -- Stochastic processes -- Markov processes Pattern recognition Rank aggregation Text feature selection Theory of computation -- Theory and algorithms for application domains -- Machine learning theory -- Markov decision processes |
| Title | Rank Aggregation Based Text Feature Selection |
| URI | https://ieeexplore.ieee.org/document/5286079 |
| Volume | 1 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB7a4sFT1VasL3IQT65N9pFsjlUsFqQUrba3ZTePUsRW-vD3O9ndtgiCeNtHDsuXzM58mcx8AFcpOhFhmfZCi-EbhtTaS30deSnNrPUzwVSeaH97Ev1-PB7LQQVutrUwxpj88Jm5dZd5Ll_P1dptlbUjP-ZUyCpUheBFrdZuP4W5xuq8YOYSzRiJRtlgZ3MflE0bGZXtUc_rdYZFv0onPVJN1ccPgZXcv3Tr__uyA2juCvXIYOuCDqFiZkdQ3yg1kNJwG-A9p7N30pkguZ7kU0Hu0HtpMsRfM3FR4HphyEsuiYMvm_DafRjeP3qlUIKXMk5XnqLcIuRZoKVmsQl1TNMwZtxobjKtFM3Q6yAZ9TXnSuDo2Oos0pG1VkYCLfQYarP5zJwA4Tg9iKTVCFLo21AaX1DLA6uUYooFLSCIVOIYwDLJCQSVyaiXIJhO0VImgd-C67-GJNliamwLGg7J5LPoq5GUIJ7-_vgM9otMjjvfdQ611WJtLmBPfa2my8Vlvhi-ARVYrRI |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTsMwEB2VggSnsoqy-oA4EbCzOPGxIFArSlVBob1ZiZcKIQrqwvczTtJWSEiIWxYfomdPZp7HMw_gLEUnElumvdBi-IYhtfZSX0deSjNr_SxmKk-0v7TjTicZDES3AheLWhhjTH74zFy6yzyXrz_UzG2VXUV-wmksVmA1CkOfFtVayx0V5lqr84KbCzRkpBpli535fVC2bWRUXPVbXqvRKzpWOvGRlVS9_5BYyT3MXe1_37YJu8tSPdJdOKEtqJjRNtTmWg2kNN0d8B7T0RtpDJFeD_PJINfovzTp4c-ZuDhwNjbkKRfFwZe78Hx327tpeqVUgpcyTqeeotwi6FmghWaJCXVC0zBh3GhuMq0UzdDvIB31NecqxtGJ1VmkI2utiGK00T2ojj5GZh8IxwlCJK1GkELfhsL4MbU8sEopplhQB4JISccBJjKnEFTIfksimE7TUsjAr8P5X0NkNn41tg47Dkn5WXTWkCWIB78_PoX1Zu-hLdutzv0hbBR5HXfa6wiq0_HMHMOa-pq-TsYn-cL4Bku4sFk |
| 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=Proceedings+of+the+2009+IEEE%2FWIC%2FACM+International+Joint+Conference+on+Web+Intelligence+and+Intelligent+Agent+Technology.+Volume+1&rft.atitle=Rank+Aggregation+Based+Text+Feature+Selection&rft.au=Wu%2C+Ou&rft.au=Zuo%2C+Haiqiang&rft.au=Zhu%2C+Mingliang&rft.au=Hu%2C+Weiming&rft.date=2009-09-01&rft.pub=IEEE&rft.isbn=9780769538013&rft.volume=1&rft.spage=165&rft.epage=172&rft_id=info:doi/10.1109%2FWI-IAT.2009.32&rft.externalDocID=5286079 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9780769538013/lc.gif&client=summon&freeimage=true |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9780769538013/mc.gif&client=summon&freeimage=true |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9780769538013/sc.gif&client=summon&freeimage=true |