A New Back-Propagation Neural Network Algorithm for a Big Data Environment Based on Punishing Characterized Active Learning Strategy

Qiuhong Zhao, Feng Ye and Shouyang Wang
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Qiuhong Zhao: School of Economics and Management, Beihang University, Beijing, China
Feng Ye: School of Economics and Management, Beihang University, Beijing, China
Shouyang Wang: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

International Journal of Knowledge and Systems Science (IJKSS), 2013, vol. 4, issue 4, 32-45

Abstract: This paper introduces the active learning strategy to the classical back-propagation neural network algorithm and proposes punishing-characterized active learning Back-Propagation (BP) Algorithm (PCAL-BP) to adapt to big data conditions. The PCAL-BP algorithm selects samples and punishments based on the absolute value of the prediction error to improve the efficiency of learning complex data. This approach involves reducing learning time and provides high precision. Numerical analysis shows that the PCAL-BP algorithm is superior to the classical BP neural network algorithm in both learning efficiency and precision. This advantage is more prominent in the case of extensive sample data. In addition, the PCAL-BP algorithm is compared with 16 types of classical classification algorithms. It performs better than 14 types of algorithms in the classification experiment used here. The experimental results also indicate that the prediction accuracy of the PCAL-BP algorithm can continue to increase with an increase in sample size.

Date: 2013
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