{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T05:15:25Z","timestamp":1733375725419,"version":"3.30.1"},"reference-count":21,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["KES"],"published-print":{"date-parts":[[2021,7,26]]},"abstract":"With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face burden in handling them. Additionally, the presence of the imbalance data in the big data is a huge concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new optimization algorithm. Here, the big data classification is performed using the MapReduce framework, wherein the map and reduce functions are based on the proposed optimization algorithm. The optimization algorithm is named as Exponential Bat algorithm (E-Bat), which is the integration of the Exponential Weighted Moving Average (EWMA) and Bat Algorithm (BA). The function of map function is to select the features that are presented to the classification in the reducer module using the Neural Network (NN). Thus, the classification of big data is performed using the proposed E-Bat algorithm-based MapReduce Framework and the experimentation is performed using four standard databases, such as Breast cancer, Hepatitis, Pima Indian diabetes dataset, and Heart disease dataset. From, the experimental results, it can be shown that the proposed method acquired a maximal accuracy of 0.8829 and True Positive Rate (TPR) of 0.9090, respectively.<\/jats:p>","DOI":"10.3233\/kes-210062","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T17:18:19Z","timestamp":1627406299000},"page":"173-183","source":"Crossref","is-referenced-by-count":4,"title":["Big data classification with optimization driven MapReduce framework"],"prefix":"10.1177","volume":"25","author":[{"given":"Mujeeb Shaik","family":"Mohammed","sequence":"first","affiliation":[{"name":"Department of CSE, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India"}]},{"given":"Praveen Sam","family":"Rachapudy","sequence":"additional","affiliation":[{"name":"G. Pulla Reddy Engineering College, Kurnool, India"}]},{"given":"Madhavi","family":"Kasa","sequence":"additional","affiliation":[{"name":"Department of CSE, JNTUA College of Engineering, Ananthapuramu, Andhra Pradesh, India"}]}],"member":"179","reference":[{"issue":"99","key":"10.3233\/KES-210062_ref1","first-page":"1","article-title":"Towards brain big data classification: epileptic eeg identification with a lightweight VGGNet on Global MIC","author":"Ke","journal-title":"IEEE Access"},{"key":"10.3233\/KES-210062_ref2","doi-asserted-by":"crossref","first-page":"7872","DOI":"10.1109\/ACCESS.2018.2797048","article-title":"Finding Top-k Dominance on Incomplete Big Data Using MapReduce Framework","volume":"6","author":"Ezatpoor","year":"2018","journal-title":"IEEE Access"},{"key":"10.3233\/KES-210062_ref3","doi-asserted-by":"crossref","unstructured":"M. Elkano, M. Galar, J. Sanz and H. 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Devi, Feature extraction using LR-PCA hybridization on twitter data and classification accuracy using machine learning algorithms, Cluster Computing, 2018, pp.\u00a01\u201310.","DOI":"10.1007\/s10586-018-2158-3"},{"key":"10.3233\/KES-210062_ref8","first-page":"1","article-title":"A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing","author":"Varatharajan","year":"2017","journal-title":"Multimedia Tools and Applications"},{"issue":"1","key":"10.3233\/KES-210062_ref9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40537-018-0115-x","article-title":"FML-kNN: scalable machine learning on Big Data using k-nearest neighbor joins","volume":"5","author":"Chatzigeorgakidis","year":"2018","journal-title":"Journal of Big Data"},{"issue":"99","key":"10.3233\/KES-210062_ref10","first-page":"1","article-title":"A Bi-objective Hyper-heuristic Support Vector Machines for Big Data Cyber-Security","author":"Sabar","year":"2018","journal-title":"IEEE Access"},{"issue":"1","key":"10.3233\/KES-210062_ref11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/TBDATA.2016.2646700","article-title":"DiP-SVM: Distribution Preserving Kernel Support Vector Machine for Big Data","volume":"3","author":"Singh","year":"2017","journal-title":"IEEE Transactions on Big Data"},{"issue":"4","key":"10.3233\/KES-210062_ref12","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s11634-016-0260-z","article-title":"Fuzzy rule based classification systems for big data with MapReduce: granularity analysis","volume":"11","author":"Fernandez","year":"2017","journal-title":"Advances in Data Analysis and Classification"},{"key":"10.3233\/KES-210062_ref13","doi-asserted-by":"crossref","unstructured":"X. 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