Journal of Guangxi Normal University(Natural Science Edition) ›› 2020, Vol. 38 ›› Issue (4): 21-31.doi: 10.16088/j.issn.1001-6600.2020.04.003

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An Improved Stack Algorithm Based on Local Sensitive Hash

WANG Junjie1, WEN Xueyan1*, XU Kesheng2, YU Ming1   

  1. 1. College of Computer and Engineering, Northeast Forestry University, Harbin Heilongjiang 150040, China;
    2. State Forestry Administration Harbin Forestry Machinery Research Institute, Harbin Heilongjiang 150086, China
  • Received:2019-11-07 Published:2020-07-13

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Abstract: Stack generalization is born with high complexity and data leakage. At the same time, when it faces different data samples, the result is not stable. The LBDS proposed in this paper uses LSH (local sensitive hashing) algorithm to map the training and test set to the hash bucket. When one of the two bucket is full, which will be used as the starting training condition, the trained model predicts the training and test data and their neighborhoods when the other bucket is full. Then the algorithm filters the base classifier by using the stability and information entropy conditions and generates the high-level classifier. Finally, through the mixed voting and average method, the results generated by high-level training prediction are obtained. Experimental results show that LBDS has an average improvement of 2% in ACC and AUC, and a decrease of 10% in training time complexity. Meanwhile, LBDS shows better stability and generalization ability.

Key words: stack generalization, locally sensitive hashing, time complexity, stability, meta classifier

CLC Number: 

  • TP301.6
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