A case study for an incremental classifier model in big data

被引:0
作者
Lincy S.B.T. [1 ]
Nagarajan S.K. [1 ]
机构
[1] School of Computer Science and Engineering, VIT, Vellore
关键词
Big data; Incremental model; Predictive analytics;
D O I
10.1504/IJCC.2019.103934
中图分类号
学科分类号
摘要
Big data is a term that implies enormous voluminous of data which cannot be handled by the existing traditional systems. With the evolving standards and technologies this volume has reached to a rate, such that even if provided with the huge amount of data it is a challenging task to obtain useful insights or knowledge out of it. Thus, this is a foremost and most important challenge for the researchers and scientists to transform the data or manipulate the data for analysis and processing them with the significant purpose of gaining insights out of it. In this paper, an incremental classifier model is applied for performing the classification with the evolving new instances of data and analysed as a case study. The experiment is carried out with the healthcare datasets to understand and analyse the suggested model and the proposed model is said to provide better performance that deals with large data. © 2019 Inderscience Enterprises Ltd.
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收藏
页码:266 / 282
页数:16
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共 28 条
  • [1] Babu S.K., Vasavi S., Nagarjuna K., Framework for predictive analytics as a service using ensemble model, IEEE 7th International Advance Computing Conference (IACC), pp. 121-128, (2017)
  • [2] Bates D.W., Saria S., Ohno-Machado L., Shah A., Escobar G., Big data in health care: Using analytics to identify and manage high-risk and high-cost patients, Health Affairs, 33, 7, pp. 1123-1131, (2014)
  • [3] Bhargava D., Poonia R.C., Arora U., Design and development of an intelligent agent based framework for predictive analytics, 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 3715-3718, (2016)
  • [4] Capuccini M., Carlsson L., Norinder U., Spjuth O., Conformal prediction in spark: Large-scale machine learning with confidence, IEEE/ACM 2nd International Symposium on Big Data Computing (BDC), pp. 61-67, (2015)
  • [5] Deepak E., Pooja G.S., Jyothi R.N., Kumar S.P., Kishore K.V., SVM kernel based predictive analytics on faculty performance evaluation, International Conference on Inventive Computation Technologies (ICICT), 3, pp. 1-4, (2016)
  • [6] Disha D.N., Sowmya B.J., Seema S., An efficient framework of data mining and its analytics on massive streams of big data repositories, IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pp. 195-200, (2016)
  • [7] Eswari T., Sampath P., Lavanya S., Predictive methodology for diabetic data analysis in big data, Procedia Computer Science, 50, pp. 203-208, (2015)
  • [8] Han D.H., Zhang X., Wang G.R., Classifying uncertain and evolving data streams with distributed extreme learning machine, Journal of Computer Science and Technology, 30, 4, pp. 874-887, (2015)
  • [9] Kavakiotis I., Tsave O., Salifoglou A., Maglaveras N., Vlahavas I., Chouvarda I., Machine learning and data mining methods in diabetes research, Computational and Structural Biotechnology Journal, 15, pp. 104-116, (2017)
  • [10] Kim J.S., Kim E.S., Kim J.H., Conceptual predictive modeling in a competitive framework using big data technology, 8th International Conference on Database Theory and Application (DTA), pp. 18-21, (2015)