SCIENCE CHINA Information Sciences, Volume 63 , Issue 8 : 182104(2020) https://doi.org/10.1007/s11432-019-2771-0

## Important sampling based active learning for imbalance classification

• ReceivedSep 26, 2019
• AcceptedJan 19, 2020
• PublishedJul 7, 2020
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### Abstract

Imbalance in data distribution hinders the learning performance of classifiers. To solve this problem, a popular type of methods is based on sampling (including oversampling for minority class and undersampling for majority class) so that the imbalanced data becomes relatively balanced data. However, they usually focus on one sampling technique, oversampling or undersampling. Such strategy makes the existing methods suffer from the large imbalance ratio (the majority instances size over the minority instances size). In this paper, an active learning framework is proposed to deal with imbalanced data by alternative performing important sampling (ALIS), which consists of selecting important majority-class instances and generating informative minority-class instances. In ALIS, two important sampling strategies affect each other so that the selected majority-class instances provide much clearer information in the next oversampling process, meanwhile the generated minority-class instances provide much more sufficient information for the next undersampling procedure. Extensive experiments have been conducted on real world datasets with a large range of imbalance ratio to verify ALIS. The experimental results demonstrate the superiority of ALIS in terms of several well-known evaluation metrics by comparing with the state-of-the-art methods.

### Acknowledgment

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61822601, 61773050, 61632004, 61972132), Beijing Natural Science Foundation (Grant No. Z180006), National Key Research and Development Program (Grant No. 2017YFC1703506), Fundamental Research Funds for the Central Universities (Grant Nos. 2019JBZ110, 2019YJS040), Youth Foundation of Hebei Education Department (Grant No. QN2018084), Science and Technology Foundation of Hebei Agricultural University (Grant No. LG201804), and Research Project for Self-cultivating Talents of Hebei Agricultural University (Grant No. PY201810).

Appendixes A–C.

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• Figure 1

(Color online) The schematic diagram of ALIS framework. The classifier is initially trained by all positive instances $P_{\rm~active}^{0}$ and equal amount of random negative instances $N_{\rm~active}^0$, and is updated iteratively according to new selected negative points or new generated positive points.

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• Table 1

Table 1Notations in ALIS framework

 Notation Description $\mathcal{P}$ Original positive class set $n^+$ The number of positive instances $\mathcal{N}$ Original negative class set $n^-$ The number of negative instances $\mathcal{D}$ Original training set and $\mathcal{D}=\mathcal{P}~\cup~\mathcal{N}$ $n$ The number of training instances and $n=n^+~+~n^-$ $\mathcal~P_{\rm~active}^j$ Generated synthetic positive class set in the $j$th iteration $\mathcal~N_{\rm~active}^j$ Selected negative class set in the $j$th iteration $\mathcal~N_{\rm~pool}$ Remaining negative class set after active selection $\mathcal~P_{\rm~active}$ Generated synthetic positive class set, $\mathcal~P_{\rm~active}~=~\bigcup_{j}~\mathcal~P_{\rm~active}^{j}$ $\mathcal~N_{\rm~active}$ Selected negative class set, $\mathcal~N_{\rm~active}~=~\bigcup_{j}~\mathcal~N_{\rm~active}^{j}$ $\omega$ A linear predictor $f$ A linear model $\lambda_1$ The trade-off parameter for controlling the margin variance $\lambda_2$ The trade-off parameter for controlling the margin mean
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Algorithm 1 Important undersampling algorithm

Input: ${\rm~Classifier}^{j}$, pool negative dataset $\mathcal~N_{\rm~pool}$, batchsize. Output: actively selected negative dataset $\mathcal~N_{\rm~active}^{j}$.

Initialize times = 0; ${\rm~ratio}_1=1$; ${\rm~ratio}_2=0$; $\mathcal~N_{\rm~pool}^{\prime}$: order $\mathcal~N_{\rm~pool}$ by the according distance between instances and decision boundary of ${\rm~Classifier}^j$;

while ${\rm~ratio}_{2}~<~{\rm~ratio}_{1}$ do

times = times + 1;

$\mathcal{N_\text{1}}~$= top $~\sharp({\rm~times}~\times~{\rm~batchsize})$ instances in $\mathcal~N_{\rm~pool}^{\prime}$;

$\mathcal~N_{2}~$= top $~\sharp((~{\rm~times}~+~1)~\times~{\rm~batchsize})$ instances in $\mathcal~N_{\rm~pool}^{\prime}$;

Calculate ${\rm~ratio}_1$ and ${\rm~ratio}_2$ of $\mathcal~N_{1}$ and $\mathcal~N_{2}$ according to (8) respectively;

end while

$\mathcal~N_{\rm~active}^{j}$ = $\mathcal~N_{1}$.

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Algorithm 2 Important oversampling algorithm

Input: $\mathcal~P_{\rm~active}$, $\mathcal~N_{\rm~active}$, $k$. Output: synthetic minority dataset $\mathcal~P_{\rm~active}^{j}$.

;

Set the bandwidth $h_i=\min~{\rm~dis}({{\boldsymbol~x}_i},{\rm~NN}({{\boldsymbol~x}_i}))$;

Identify informative minority-class set $\mathcal{P^\text{info}}$ via (9);

for ${\boldsymbol~x}_{i}~\in~\mathcal{P^\text{info}}$

Set the mixture weight $\xi_i$ via (11)

• Table 2

Table 2Description of the datasets

 Dataset $n$ $m$ $n^-$ $n^+$ ratio ($\frac{n^-}{n^+}$) haberman 306 3 225 81 2.8 libra 360 90 288 72 4 glass6 214 9 185 29 6.38 ecoli3 336 7 301 35 8.6 yeast0256vs3789 1004 8 905 99 9.14 Satimage 6435 36 5809 626 9.27 balance 625 4 576 49 11.8 shuttlec0vsc4 1829 9 1706 123 13.87 Letter-a 20000 16 19211 789 24.34 yeast4 1484 8 1433 51 28.1 yeast6 1484 8 1449 35 41.4 abalone19 4174 7 4142 32 129.44
• Table 3

Table 3Analysis of variance (ANOVA) test and winning times of pairwise t-test (in bracket) between ALIS and the baseline on twelve real-world datasets

 Metric haberman libra glass6 ecoli3 yeast0256vs3789 Satimage Precision-majority 4.79E$-$03 (4) 1.18E$-$06 (4) 0.64 (0) 3.86E$-$10 (2) 2.48E$-$05 (2) 1.36E$-$46 (5) Recall-minority 9.36E$-$10 (4) 2.38E$-$06 (2) 0.62 (0) 1.21E$-$10 (2) 7.28E$-$09 (4) 5.19E$-$31 (5) F$_{\rm~macro}$ 2.79E$-$07(5) 0.0020(2) 0.0011(2) 5.44E$-$02(4) 1.27E$-$08(3) 2.00E$-$36(5) AUC 0.033 (2) 3.45E$-$09 (3) 1.79E$-$09 (2) 5.45E$-$15 (3) 7.83E$-$09 (3) 2.59E$-$06 (3) Metric balance shuttlec0vsc4 Letter-a yeast4 yeast6 abalone19 Precision-majority 8.37E$-$05 (4) 7.20E$-$12 (4) 8.70E$-$09 (4) 4.36E$-$13 (3) 1.30E$-$10 (3) 0.15 (3) Recall-minority 4.53E$-$05 (3) 4.14E$-$12 (4) 1.82E$-$08 (4) 6.85E$-$17 (3) 5.23E$-$14 (3) 1.32E$-$07 (3) F$_{\rm~macro}$ 0.0249(1) 1.40E$-$07(4) 1.79E$-$27(4) 2.74E$-$08(2) 3.72E$-$06(2) 0.0428(0) AUC 2.01E$-$06 (2) 5.15E$-$17 (2) 8.43E$-$22 (3) 0.8846 (1) 4.07E$-$05 (2) 4.48E$-$06 (2)

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