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Averaging versus voting: A comparative study of strategies for distributed classification

  • * Corresponding author: Qiang Wu

    * Corresponding author: Qiang Wu
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  • In this paper we proposed two strategies, averaging and voting, to implement distributed classification via the divide and conquer approach. When a data set is too big to be processed by one processor or is naturally stored in different locations, the method partitions the whole data into multiple subsets randomly or according to their locations. Then a base classification algorithm is applied to each subset to produce a local classification model. Finally, averaging or voting is used to couple the local models together to produce the final classification model. We performed thorough empirical studies to compare the two strategies. The results show that averaging is more effective in most scenarios.


    Note: The second author Honglan Xu’s affiliation is added online.

    Mathematics Subject Classification: Primary: 68T05, 68T09, 68W15.


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  • Table 1.  Description of Data Sets and Classification Tasks

    Classification Task Number of Observations Number of Features
    Default of Credit Card Clients 30,000 23
    Wilt Diseased Tree Detection 4,889 5
    APS Failure 60,000 170
    MAGIC Gamma Telescope 19,020 10
    Spam Email Detection 4,601 57
    Epileptic Seizures 9,200 178
    Wireless Localization {1, 2} vs {3, 4} 2,000 7
    Student Evaluation {1, 2} vs {3, 4, 5} 5,046 32
    Handwritten Digits 5 vs 8 12,017 786
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    Table 2.  Classification accuracy (in percentage) of distributed logistic regression and p-values of hypothesis tests on the difference between voting and averaging strategies

    Classification Task Voting Averaging p-value
    Default of Credit Card Clients 73.71 80.15 <2.2e-16
    Wilt Diseased Tree Detection 95.42 96.94 <2.2e-16
    APS Failure 98.39 98.75 <2.2e-16
    MAGIC Gamma Telescope 79.18 79.18 0.9845
    Spam Email Detection 61.52 92.83 <2.2e-16
    Epileptic Seizure 50.10 66.11 <2.2e-16
    Wireless Localization {1, 2} vs {3, 4} 91.77 95.15 <2.2e-16
    Student Evaluation {1, 2} vs {3, 4, 5} 91.81 95.17 <2.2e-16
    Handwritten Digits 5 vs 8 84.46 95.84 <2.2e-16
     | Show Table
    DownLoad: CSV

    Table 3.  Classification accuracy (in percentage) of distributed SVM and p-values of hypothesis tests on the difference between voting and averaging strategies

    Classification Task Voting Averaging p-value
    Default of Credit Card Clients 79.29 79.48 9.2e-05
    Wilt Diseased Tree Detection 96.83 97.19 4.6e-08
    APS Failure 98.52 98.60 <2.2e-16
    MAGIC Gamma Telescope 86.59 86.64 0.2107
    Spam Email Detection 93.20 93.47 0.0001
    Epileptic Seizure 89.16 89.46 0.0008
    Wireless Localization {1, 2} vs {3, 4} 95.42 95.47 0.3773
    Student Evaluation {1, 2} vs {3, 4, 5} 95.31 95.34 0.6433
    Handwritten Digits 5 vs 8 99.50 99.54 2.3e-05
     | Show Table
    DownLoad: CSV
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