{"_buckets": {"deposit": "23c50a96-bf9e-4854-b81d-fbd1016142f0"}, "_deposit": {"created_by": 45, "id": "6640", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "6640"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00006640", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Efficient Classification of Concept Drift in Data Stream", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "The classification in data streams is widely studied in\nthe literature over the last decade. In recent literature,\nmany research contributions use incremental or\nprogressive learning strategies to classify the data\nstreams. Stream classification is a variant of incremental\nlearning of classifiers that has to satisfy requirements\nspecific for massive streams of data. There are many\nmethods such as single classifiers, windowing techniques,\ndrift detectors and ensemble methods. The classifier\nensembles provide a way of adapting to changes by\nmodifying ensemble components or their aggregation\nmethod. Adaptive Classifier Ensemble (ACE) method use\nto provide a natural way of adapting to change by\nmodifying ensemble members. This method improves\nmore accuracy and adaptable than other ensemble\nmethods."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Data Stream Mining"}, {"interim": "Classification"}, {"interim": "Concept drift"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-11-26"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Efficient Classification of Concept Drift in Data Stream.pdf", "filesize": [{"value": "165 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 165000.0, "url": {"url": "https://meral.edu.mm/api/files/23c50a96-bf9e-4854-b81d-fbd1016142f0/Efficient%20Classification%20of%20Concept%20Drift%20in%20Data%20Stream.pdf"}, "version_id": "6fcd6f80-0a32-4e17-9ba0-d83eeef23440"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICAIT-2017", "subitem_c_date": "1-2 November, 2017", "subitem_conference_title": "1st International Conference on Advanced Information Technologies", "subitem_place": "Yangon, Myanmar", "subitem_session": "Workshop Session", "subitem_website": "https://www.uit.edu.mm/icait-2017/"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Ei Thwe Khaing"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2017-11-02"}, "item_title": "Efficient Classification of Concept Drift in Data Stream", "item_type_id": "21", "owner": "45", "path": ["1605779935331"], "permalink_uri": "https://meral.edu.mm/records/6640", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-11-26"}, "publish_date": "2020-11-26", "publish_status": "0", "recid": "6640", "relation": {}, "relation_version_is_last": true, "title": ["Efficient Classification of Concept Drift in Data Stream"], "weko_shared_id": -1}