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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Knowledge-based and Intelligent Engineering Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.