{"created":"2020-09-01T14:30:01.204777+00:00","id":4313,"links":{},"metadata":{"_buckets":{"deposit":"6a0cd946-1dda-4490-92b9-454b4c37ec47"},"_deposit":{"id":"4313","owners":[],"pid":{"revision_id":0,"type":"recid","value":"4313"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/4313","sets":["1582963302567:1597824322519"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Feature Selection and MapReduce Based Neural Network Classification for Big Data","subitem_1551255648112":"en_US"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Nowadays, a large amount of digital data is generated from everywhere, everysecond of the day. One of the challenges is the volume of generated data with highdimensionality. Most of traditional machine learning algorithms are not good in trainingtime and classification result to find hidden insights from these high dimensional data.Backpropagation Neural Network, one of the most popular Artificial Neural Networks,is widely used in many classification applications. To reduce the data dimension,feature selection is needed to consider. MapReduce is a software framework for writingapplications which are run on Hadoop that supports rapid computation and processingof Big Data.First, the data preprocessing is performed by substituting missing values. Andthen, the dimension of data is reduced using Chi-square feature selection method. Afterthat, Backpropagation Neural Network with MapReduce paradigm is used forclassification. For this MapReduce-based Neural Network classifier, it is constructedusing one and two hidden layers. The outputs of the proposed system are theperformance measures which involve the training time, accuracy and number ofselected features. The experiments have made with feature selection and without featureselection. Then, the results are compared with the results obtained from WEKA tooland Conventional Backpropagation Neural Network. Six different datasets (ThyroidDisease Diagnosis, Diabetics Diagnosis, Insurance Classification, Intrusion Detection,Customer Churn Prediction and Human Activity Recognition) are used as case study.Based on the experimental results, the MapReduce-based Neural Network algorithmgives the superior efficiency in training time faster than the WEKA tool in large dataset.And it is also found that feature selection can retain a suitably accuracy in representingthe original features by selection a minimal feature subset from a problem domain. Theproposed system is implemented by Java programming language on Linux platform."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value":[]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-09-23"}],"displaytype":"preview","filename":"FeatureSelectionAndMapReduceBasedNeuralNetworkClassificationForBigData(FinalVersion).pdf","filesize":[{"value":"3968 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/4313/files/FeatureSelectionAndMapReduceBasedNeuralNetworkClassificationForBigData(FinalVersion).pdf"},"version_id":"8e6dd8e7-2073-40db-8d07-4433e32971e6"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"","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":"University of Computer Studies, Yangon","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Shine, Chit Thu"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Thesis"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2018-12"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/2252"},"item_title":"Feature Selection and MapReduce Based Neural Network Classification for Big Data","item_type_id":"21","owner":"1","path":["1597824322519"],"publish_date":"2019-09-23","publish_status":"0","recid":"4313","relation_version_is_last":true,"title":["Feature Selection and MapReduce Based Neural Network Classification for Big Data"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T03:06:05.138427+00:00"}