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Classification Method of Warehouse Inventory Data Based on PE-AdaBoost Algorithm

Junyang Liu

Postgraduate Brigade, Air Force Logistics Academy, Xuzhou, China.

*Corresponding author: Junyang Liu

Published: 28 November 2022 How to cite this paper


In view of the problems existing in traditional warehouse inventory data mining, such as low classification efficiency, lack of dynamic adjustment ability of weak classifiers, and excessive weight of samples with multiple classification errors, a perceptual enhanced classification algorithm based on AdaBoost is proposed. First, the multi-layer perceptual network is used to dynamically adjust the weight of the weak classifier, complete the sample weighting in the first perceptual network, and complete the weight calculation of the weak classifier in the second perceptual network, and obtain the best weight. Then, the weight attenuation superparameter is introduced to process the sample weight, which is used to solve the phenomenon that the samples with multiple classification errors are set with too high weights. Through the comparison with other warehouse dataset classification methods, it is found that this method can improve the accuracy of the current classification methods, and is superior to other algorithms in terms of running performance.

KEYWORDS: AdaBoost, Perceptual enhancement, Neural network, warehouse inventory, mul-ti-class classification


[1] PARTOVI F Y, ANANDARAJAN M. Classifying inventory using an artificial neural network approach [J].  Computers and Industrial Engineering, 2002(41):389-404. 

[2] PARK J, BAE H, BAE J. Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification [J].  Computers and Industrial Engineering, 2014, 76(1):40-48. 

[3] Yang Xueqiang, Li Wenjun, Yue Yong.  Method for determining the weight of comprehensive evaluation index [J].  Journal of armored corps Engineering College, 2015, 29 (1): 101-105. 

[4] LOLLI F, ISHIZAKA A, GAMBERINI R. New AHP-based approaches for multi-criteria inventory classification [J].  International Journal of Production Economics, 2014(156): 62-74. 

[5] TSAI C Y, YEH S W. A multiple objective particle swarm optimization approach for inventory classification [J].  International Journal of Production Economics, 2008(114): 656-666. 

[6] Li Bo, Zhao Zhiyan, Duan Tieying.  A hybrid forecasting method for multi criteria inventory classification [J].  Computer integrated manufacturing system, 2004, 10 (5): 594-599. 

[7] SUCAR L E, BIELZA C, MORALES E F, et al.  Multi-label classification with Bayesian network-based chain classifiers [J].  Pattern Recognition Letters, 2014(41):14-22. 

[8] Li Hang.  Statistical learning method [M].  Tsinghua University Press, 2012. 

[9] Liu Jianming, Zhang Jie, Lei Jie, Liao zhouyu.  Twin support vector machine face recognition method based on AdaBoost [J].  Sensors and Microsystems, 2020, 39 (07): 51-53 + 57.  Doi: 10.13873/j. 1000-9787 (2020) 07-0051-03. 

[10] Pi Jiali, Wu Zhengzhong, Chen Zhuo.  Target tracking and recognition based on AdaBoost-CSHG [J].  Computer science, 2016,43 (04): 318-321. 

[11] Sylvain Gugger, Jeremy Howard.  AdamW and Super-convergence is now the fastest way to train neural nets [EB/OL].  https: //, 2018-02-07.

How to cite this paper

Junyang Liu. Classification Method of Warehouse Inventory Data Based on PE-AdaBoost Algorithm. OA Journal of Engineering & Technology, 2022, 1(2), 22-28.

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