Log in
Language:

MERAL Myanmar Education Research and Learning Portal

  • Top
  • Universities
  • Ranking
To
lat lon distance
To

Field does not validate



Index Link

Index Tree

Please input email address.

WEKO

One fine body…

WEKO

One fine body…

Item

{"_buckets": {"deposit": "01d80141-3d6b-4c4e-ba76-6f3af7cafbe7"}, "_deposit": {"id": "4677", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4677"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4677", "sets": ["1597824273898", "user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Reducing the Size of Feature Set by Using Modified MCA", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Reducing the size of a feature set, withoutaltering the original representation, is anessential data processing step prior to applyinga learning algorithm. The removal of irrelevantand redundant information improves theperformance of machine learning algorithms. Inthis paper, Modified-Multiple CorrespondenceAnalysis (Modified-MCA) is introduced. Itintegrates the correlation and reliabilityinformation between each feature and eachclass. Moreover, the proposed methodcontributes the optimal p-value to improve thereliability. To evaluate the performance ofproposed method, experiments are carried out onten benchmark datasets. In the experiments,three classifiers namely AdaBoost, DecisionTable, JRip are used to verify that the outputfeature dataset produced by proposed methodoutperforms. Using three different classifiers isto get more accurate average classificationresults than using one classifier. The proposedModified-MCA demonstrates reducing the size ofthe feature subspace and promisingclassification results. Moreover, the resultsperforms that the propose method is better than other well-known feature selection methods;MCA, Information Gain and Relief."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Feature Selection"}, {"interim": "Correlation"}, {"interim": "Reliability"}, {"interim": "P-value"}, {"interim": "Confidence Interval"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value": []}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Fourteenth International Conference On Computer Applications (ICCA 2016)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Khaing, Myo"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2016-02-25"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/337"}, "item_title": "Reducing the Size of Feature Set by Using Modified MCA", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004677", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "4677", "relation": {}, "relation_version_is_last": true, "title": ["Reducing the Size of Feature Set by Using Modified MCA"], "weko_shared_id": -1}
  1. University of Computer Studies, Yangon
  2. Conferences

Reducing the Size of Feature Set by Using Modified MCA

http://hdl.handle.net/20.500.12678/0000004677
http://hdl.handle.net/20.500.12678/0000004677
b16a4ad8-5da5-43db-8dd3-a1b2ae756ed9
01d80141-3d6b-4c4e-ba76-6f3af7cafbe7
Publication type
Article
Upload type
Publication
Title
Title Reducing the Size of Feature Set by Using Modified MCA
Language en
Publication date 2016-02-25
Authors
Khaing, Myo
Description
Reducing the size of a feature set, withoutaltering the original representation, is anessential data processing step prior to applyinga learning algorithm. The removal of irrelevantand redundant information improves theperformance of machine learning algorithms. Inthis paper, Modified-Multiple CorrespondenceAnalysis (Modified-MCA) is introduced. Itintegrates the correlation and reliabilityinformation between each feature and eachclass. Moreover, the proposed methodcontributes the optimal p-value to improve thereliability. To evaluate the performance ofproposed method, experiments are carried out onten benchmark datasets. In the experiments,three classifiers namely AdaBoost, DecisionTable, JRip are used to verify that the outputfeature dataset produced by proposed methodoutperforms. Using three different classifiers isto get more accurate average classificationresults than using one classifier. The proposedModified-MCA demonstrates reducing the size ofthe feature subspace and promisingclassification results. Moreover, the resultsperforms that the propose method is better than other well-known feature selection methods;MCA, Information Gain and Relief.
Keywords
Feature Selection, Correlation, Reliability, P-value, Confidence Interval
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/337
Journal articles
Fourteenth International Conference On Computer Applications (ICCA 2016)
Conference papers
Books/reports/chapters
Thesis/dissertations
Back
0
0
views
downloads
See details
Views Downloads

Versions

Ver.1 2020-09-01 15:15:58.022853
Show All versions

Share

Mendeley Twitter Facebook Print Addthis

Export

OAI-PMH
  • OAI-PMH DublinCore
Other Formats
  • JSON

Confirm


Back to MERAL


Back to MERAL