SCIENTIA SINICA Informationis, Volume 48, Issue 12: 1681-1696(2018) https://doi.org/10.1360/N112018-00138

A personalized mail re-filtering system based on the client

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  • ReceivedMay 28, 2018
  • AcceptedAug 22, 2018
  • PublishedDec 4, 2018


Email is an essential communication tool, but a large number of spam emails can seriously affect the work and life of users and can even cause property damage. Due to different interests and hobbies, there may be huge differences in the definition of spam by users; the realization of personalized spam filtering has become an important issue in the field of spam filtering. When emails are misjudged, the user has to manually modify it, which brings great inconvenience to the user experience. In order to effectively solve the above problems and realize the functions of personalized email filtering and automatic correction of mis-filtered emails, this paper combined with rules and statistical methods presents a personalized email re-filtering system based on the client (PRFC) and implements the automatic modification of the mis-filtered emails. A large part of existing spam filters do not consider the difference between class prior probability and class imbalance problem; they only filter the mail online. Firstly, the proposed filter system processes the mails entering the inbox and the garbage and then designs two mutually learned filters based on the multi-task learning principle to be used for the automatic modification of the mis-filtered emails in inbox and garbage. To ensure the performance of the filter based on the interests of users and data distribution of mails varying with time, a multi-window learning framework that combines important weights to effectively implement the dynamic adaptation of the filter was designed. Finally, our proposed filtering system on the TREC 2006c and 2007p data sets that gets a significant filtering efficiency was verified.

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    Algorithm 1 PRFC实现

    Require:有真实标记样本$\{{\rm~SW}^{i}\}_{i=1}^{N_m}$, 无真实标记样本$\{{\rm~TW}^{(i)}\}_{i=1}^{N_m}$, 解析后测试邮件email, LW的起始位置$T_0$和当前位置$T_1$, L模型的可接受错滤率阈值$\rho$, 预测标记的置信度阈 值$\xi$, 已初始化的过滤器Filter_inbox和Filter_junkbox;

    if 根据email`From'或email`Re'并基于规则能判定$y=0$ then

    return $y$;



    基于主题过滤: 向量化email`Subject'为$x^s$;



    if ${\rm~SW}$中出现类不平衡 then



    end if



    if 检测到协变量漂移发生 then


    end if


    if ${\rm~Err}(L)>~{\rm~Err}(S)$且${\rm~Err}(L)>\rho$ then





    end if


    if ${\rm~confidence}>\xi$ then

    return $y$;


    基于正文过滤: 向量化email`Body'为$x^b$;

    同理, 重复6$\sim$24;

    return $y$;

    end if



    同理, 重复5$\sim$31;

    end if

  • Table 1   Experimental corpuses
    Corpus Normal Spam Total
    TREC 2006c 21766 42854 64620
    TREC 2007p 25220 50199 75419
  • Table 2   Multi-task vs. single task $^{\rm~a)}$
    Method G-mean ($\uparrow$) $F1$ ($\uparrow$) ($1-$ROCA)% ($\downarrow$) Accuracy ($\uparrow$)
    Multi-task 0.9895 0.9921 0.0104 0.9896
    Single task 0.9703 0.9775 0.0296 0.9705
  • Table 3   Evaluating different algorithms on TREC 2006c and TREC 2007p $^{\rm~a)}$
    Corpus TREC 2006c TREC 2007p
    Evaluation Accuracy FPR ($1-$ROCA)% lam% Accuracy FPR ($1-$ROCA)% lam%
    criteria ($\uparrow$) ($\downarrow$) ($\downarrow$) ($\downarrow$) ($\uparrow$) ($\downarrow$) ($\downarrow$) ($\downarrow$)
    DISvm [34] 0.9594 0.0107 0.0383 2.73 0.9658 0.0087 0.0321 2.10
    ROSVM [35] 0.9935 0.0036 0.0094 0.34 0.9848 0.0060 0.0108 0.86
    MLC [36] 0.9992 0.0021 0.0004 0.08 0.9855 0.0056 0.0096 0.64
    PRFC 0.9984 0.0013 0.0025 0.12 0.9865 0.0053 0.0068 0.45

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