Abstract
Big Data is dealing with two major issues witch are the adaptation and scaling of data analysis techniques at the Big Data level by one side, such as the implementation of distribute Machine Learning algorithms. In another side, the reduction of data sets so that a processing can be performed using the existing Machine Learning techniques. It consists to determine a smallest and sufficient training set size that obtains the same accuracy as the entire available dataset. The proposed approach deals on the selection of instances number needed to be presented for data mining algorithms. GDAS is one of the adaptive sampling algorithms that can scale down the data. In this paper, an improved GDAS algorithm based on BLB technique and ScaSRS algorithm is presented, which substantially allows a better scalability and performances in terms of time needed. As validated by experiments on various datasets, our approach can achieve very prominent improvement in efficiency and also the resulting accuracy over previous works.
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Djouzi, K., Beghdad-Bey, K., Amamra, A. (2022). A Scalable Adaptive Sampling Based Approach for Big Data Classification. In: Senouci, M.R., Boulahia, S.Y., Benatia, M.A. (eds) Advances in Computing Systems and Applications. CSA 2022. Lecture Notes in Networks and Systems, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-12097-8_7
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