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http://dx.doi.org/10.22156/CS4SMB.2018.8.1.201

A Study on Adaptive Learning Model for Performance Improvement of Stream Analytics  

Ku, Jin-Hee (Division of Information Communication Convergence Engineering, Mokwon University)
Publication Information
Journal of Convergence for Information Technology / v.8, no.1, 2018 , pp. 201-206 More about this Journal
Abstract
Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.
Keywords
Adaptive Learning; Machine Learning; Nearest Neighbor Algorithm; Stream Analytics; Artificial Intelligence;
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Times Cited By KSCI : 2  (Citation Analysis)
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