ROGA: Random Over-sampling Based on Genetic Algorithm
Authors: ZONGDA HAN
XIUQUAN QIAO
SHUBO ZHAN
Venue:  (2021)
Keywords: class imbalance
over-sampling
genetic algorithm

Abstract: When using machine learning to solve practical tasks, we often face the problem of class imbalance. Unbalanced classes will cause the model to generate preferences during the learning process, thereby ignoring classes with fewer samples. The oversampling algorithm achieves the purpose of balancing the difference in quantity by generating a minority of samples. The quality of the artificial samples determines the impact of the oversampling algorithm on model training. Therefore, a challenge of the oversampling algorithm is how to find a suitable sample generation space. However, too strong conditional constraints can make the generated samples as non-noise points as possible, but at the same time they also limit the search space of the generated samples, which is not conducive to the discovery of better-quality new samples. Therefore, based on this problem, we propose an oversampling algorithm ROGA based on genetic algorithm. Based on random sampling, new samples are gradually generated and the samples that may become noise are filtered out. ROGA can ensure that the sample generation space is as wide as possible, and it can also reduce the noise samples generated. By verifying on multiple datasets, ROGA can achieve a good result.

Under review as a conference paper at ICLR 2021
ROGA: RANDOM OVER-SAMPLING BASED ON GE- NETIC ALGORITHM
Anonymous authors Paper under double-blind review
ABSTRACT
When using machine learning to solve practical tasks, we often face the problem of class imbalance. Unbalanced classes will cause the model to generate prefer- ences during the learning process, thereby ignoring classes with fewer samples. The oversampling algorithm achieves the purpose of balancing the difference quantity by generating minority of samples. The quality of the artificial samples determines the impact of the oversampling algorithm on model training. There- fore, challenge of the oversampling algorithm is how to find a suitable sample generation space. However, too strong conditional constraints can make the gen- erated samples as non-noise points possible, but at the same time they also limit the search space of the generated samples, which is not conducive to the discov- ery of better-quality new samples. Therefore, based an oversampling algorithm ROGA based on genetic algorithm. Based on random sampling, new samples are gradually generated and the samples that may become noise are filtered out. ROGA can ensure that the sample generation space is as wide as possible, and it can also reduce the noise samples generated. By verifying multiple datasets, ROGA can achieve good result.
this problem, we propose
INTRODUCTION
When modeling the classification problem, the balance of classes can ensure that the information balanced during the learning process of the model, but the classes are often unbalanced in ac- tual tasks(Kotsiantis et al.) (2005]; & Garcia (2009)). which leads to the machine learning model preferring majority class samples. In order to solve this problem, commonly used method uses oversampling algorithm to increase the minority class samples to balance the gap between the mi- nority class samples and the majority class samples. Therefore, the quality of the generated samples will determine the training quality of the model after oversampling, but it is difficult to characterize this effect. In the past studies, oversampling methods only can estimated the noise samples, such as overlapping samples or outliers, and eliminated it much as possible. Ensure that the sample does not make training the model more difficult.
SMOTE/Chawla et al. (2002)) and its derivative algorithms generate samples by interpolation. There has been lot of work in the selection of samples to be interpolated, the number of interpola- tions, or the method of interpolation, and the final goal is reduce the generation of noise samples by restricting the interpolation position. Some researchers pointed out that sampling should not be performed in the sample space, but should be projected to other spaces (Wag al. (2007). Zhang & Yang (018)). In addition, some researchers have proposed that sampling should be taken from the minority distribution (Bellinger et al.] (2013), Das et al. (2015)) ensure the consistency of the distribution. It can be found that in order to reduce the generation of noise samples, the space for generating samples needs be restricted, that is, the oversampling algorithm can only sample within a limited range.
However, just paying attention to noise generation may not achieve better results. Too strong con- straints will cause the sample generation space to be limited, and the generated samples will fall into a local optimal solution, so that it is impossible to find samples that are more conducive to model training. Therefore, in order to reduce the limitation on the sample generation space and reduce noise as much as possible, this paper proposes oversampling algorithm ROGA based on genetic al- gorithm. ROGA will randomly sample in the feature space before the first iteration, and generate

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set of artificial samples the basic population for the next iteration of the genetic algorithm. Ran- dom sampling will increase the noise in the samples, but it will also prevent ROGA from falling into local optimal solution. In order to judge the noise, ROGA will calculate the fitness of each sample based on the Gaussian similarity with surrounding neighbors. The fitness represents the possibil- ity that the sample is noise sample. In the iterative process, new samples are generated through crossover and mutation operations, and samples with lower fitness are gradually removed, so as to reduce noise.
ROGA will generate new samples in the entire feature space, mainly because the initial population generated by random sampling in the entire feature space. The crossover and mutation operations of genetic algorithms will continuously change the samples in the population to find samples with higher fitness values, and the screening mechanism will help us eliminate noisy samples in the population. Therefore, ROGA can balance the wide generation space and noise generation. Through experiments on multiple datasets, we found that ROGA can achieve the best scores on some datasets on the Fl-score.
RELATED WORK
2.1 CLASS IMBALANCE
Data-based methods are represented by oversampling and undersampling. In order to balance the gap in the number of samples, oversampling generates new minority class samples, while undersam- pling(Arefeen al. (2020); Koziarski (2020) reduces redundant majority class samples.
The algorithm- based method is to improve the model algorithm to make it more suitable for training on unbalanced data. The first improvement measure is to balance the loss caused by the major- ity class and the minority class in the cost-sensitive loss function. Cost-sensitive can be combined with various machine learning algorithms, such as Adaboost Wei (1999). SVM(Lin (2002)) Deci- sion Tree(Ling et al. (2003)), cross entropy(Li & (2019)) and so on. The second improvement measure is integrated learning technology. Liu et.al(Liu al. (2009) proposed EasyEnsemble and BalanceCascade, performed multiple under- under-sampling and integrated multiple models. Some re- searchers have added the ideas of Bagging Wang & Yao (2009) and Coosting(Chavae et al. (2003) on SMOTE.
2.2 OVERSAMPLING
The oversampling algorithm is to generate new minority class samples to balance the quantitative difference between classes. The current oversampling algorithm can be divided into random sam- pling method, interpolation method, distributed sampling method and copy replacement method.
The random sampling method to perform random sampling in the feature space, and the generated samples can easily deviate from the distribution of the original samples and produce noisy data. The interpolation oversampling algorithm represented by SMOTEChawla et al.) (2002) restricts the generation of samples between two samples, and generates new minority class samples through linear interpolation. On the basis of the SMOTHChawla et al. 2002) algorithm, a large number of improved algorithms have appeared.
First, in order to further reduce the generation of noise, improvements can be made in the selec- tion of the sample to be interpolated, the interpolation weight of the sample, and the position of the BOOTT--BorderlineNggue
et al. (20TT) believes that the minority class samples located at the decision boundary should be selected, and some researchers believe that interpo- lation should be performed within the minority class sample clusters generated by the clustering algorithm, such as KMenns-SMON SMOTEMMOOTEEE SMOTE(Douzas & Bacao (2018)) CURE-SMOTE(Ma & Fan 2017h) et al. (2012). (OMOaDouzas & Bacao (2017b)) etc. ADAS (He et al. (2008), ). SMOTE-D Torres et al. (2016) and MSMOTE et al. (2009) reduce the num- ber of interpolations for some samples by setting the weight of the sample. When Safe-level SMOTE (unnkhupopah et al. (2009) generates samples, it will be closer to more regions of similar samples. In addition to controlling the noise generation in the generation stage, you also filter the noises in the samples after generating the samples, such as SMOTE-FRSTE (ereeverbiest et al. 2.12)). SOOSE et al. (2155). MDO( Abdi & Hashemi (015)). etc.
interpolation.
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Second, the above methods all use linear interpolation when generating samples, but linear inter- polation has limitations. Therefore, an improved algorithm for generating samples by nonlinear interpolation has appeared. Random-SMOTT((Dorgg & Wang (2011)) performs interpolation in a tri- angular area formed by three samples, and Geometric AMOTHeDouzas Bacao 7017a) generates samples in geometric
instead of a linear area.
Third, choose a more appropriate generation space, such as projecting to low-dimensional space(Wang et al. 2007)) or high-dimensional space(Zhang & Yang (2018). Fourth, when selecting neighbor samples, other indicators can be used to measure the distance between samples, such as cosine d Koto (2014)), propensity score al. (2014),
seeanddinnnniigborrs Garcia
et al. (2012) etc.
The distribution sampling method learn the distribution function density function of minority class samples, and then sample from the distribution function to obtain a new artificial sample. DEAGO(Bellinger et al. (2015)) generates samples by denoising autoencoder, while RACOG and WRACOG(Das al. all]((20)))))))))) (2013) used the dependency tree algorithm to estimate the discrete probability distribution, and then generated artificial samples from the probability distribution through Gibbs sampling. The copy replacement method is copy safe minority class samples and eliminate unsafe majority class samples(Stefanwsss) Wilk 20088. Napierala et al. (2010)).
2.3 GENTIC ALGORITHM
Genetic algorithm (GA) is a bionic algorithm proposed by Holland(Holland (1975) in 1975. Its main idea to simulate the inheritance and mutation of genes during human evolution, and to control the evolution direction of samples through the fitness function. As heuristic algorithm, it is often used in optimization problems(Rahnnn Rahmatsmii & Michielssen (1999). |oonseca Fleming (1993), Wright (1990), Homaifar et al. (1994), using the entire problem space the search space, and searching for the best value in the search space. Genetic algorithms are also often used to optimize machine learning algorithms, such SVM(Kim et al. (2005),, K-msans(Krishna & Narasimhal Murty 1999)). Decision Tree et al. (9955), ) neural network network(Montang (1989)). etc.
Genetic algorithm relies on randomness in the optimization process, which means that the speed of finding the optimal solution is unstable, but the genetic algorithm will not get stuck in a local solution when the fitness function is appropriate. In the process of solving, because it does rely on the additional knowledge, its search range is wide. It is for this reason that we believe that genetic algorithms can find great patterns that have not yet been discovered.
3 ROGA
3.1 GA
3.1.1
BASIC POPULATION
In the first iteration, this paper randomly generated N artificial samples as the basic population. In each subsequent iteration, the artificial samples generated in the previous iteration will be used as the basic population for this iteration.
3.1.2 FITNESS
The consideration of the degree of adaptation of the sample is mainly based its attribution minority class sample. The fitness value of this sample calculated by the Gaussian similarity with the surrounding samples, which is computed as:
filness (x.) = Σn yJ sim (W4,,
E 55 m8
sim (I:; 2i))
2
(2)
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Iy is the sample that currently needs calculate fitness. K is the number of neighbors to choose and set to 5 in this paper. y/ E (-1,1). If x4 and 2; belong to the same sample, then Vj = 1. If they belong to the heterogeneous sample, then Yj -L is the standard deviation of Gaussian kernel and set to in this paper.
3.1.3 SELECTION
Before each crossover and mutation operation, M candidates need to be selected from the population based fitness. The candidates for crossover operation and mutation operation are M samples with the lowest fitness in the population, and M must be an even number. In the traditional genetic algorithm, the crossover operation should select the sample with the highest fitness in the population, hoping that the excellent genes can be inherited to the offspring. However, in the generation of minority samples, each feature value of the sample cannot establish mapping relationship with the quality of the sample. Therefore, it is impossible to achieve the purpose of inheriting excellent genes when performing crossover operations. On the contrary, will degrade the samples with high fitness in the population. Therefore, this paper only selects samples with low fitness to perform crossover and mutation operations and retains samples with high fitness in the current population.
3.1.4 CROSSOVER
Each crossover operation will generate new samples from two candidate samples. For each pair of candidate samples(ri, each sample contains m feature values Ię = J/2, Jim j. and p feature values are randomly selected. For any pair of features J's and fsk= new sample is generated
for linear interpolation, as shown in Eq. 3 and Eq. 4
f'x = a * fu + (1 1--a] ) # fjt
(3)
fjx = a * fak + (1 a) fik
(4)
E [0, 1] is randomly generated.
3.1.5 MUTATION
Each mutation operation will get new sample. For each candidate sample x; [fil fi2................. fim }. randomly select p feature values in the candidate sample, and multiply the selected feature value with randomly generated / e [0, 2] as the feature value the new sample at that position, as shown in Eq. 5 The unselected feature value directly inherits the feature value of the original sample that position.
f/. = 3 fsk
(5)
3.1.6 UPDATE
After the crossover and mutation operations, 3M new samples will be obtained. After calculating the fitness of all samples, the N highest-fitting samples are selected the basic population for the next iteration.
3.2 ROGA
The ROGA algorithm is divided into two stages, a random sampling stage and a genetic algorithm stage. The pscudo code is shown in Algorithm[]] In the random sampling stage, random sampling performed in the feature space to obtain the required initial population P. At this stage, the number of samples in the population needs to be calculated first. In this paper, the number of basic populations N Nayn set to the difference quantity between majority class samples and minority class samples. The method of random sampling is that randomly samples value within its range of values for each feature f E {.f1, fa.fff...... fa...., . In the genetic algorithm stage, new artificial samples are generated in each iteration based the obtained basic population and the artificial samples with low fitness values are eliminated. First, you need to determine the candidate samples for crossover and mutation operations. Calculate the fitness of each sample in the current population
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Table 1: Dataset Description
Name
Ratio
Number
Feature Number
Ecoil
8.6:1
336
optical.digitt
9.1:1
5620
64
Satimage
9.3:1
6435
36
pen digits
9.4:1
10992
16
sick_euthyroid
9.8:1
3163
42
spectrometer
11:1
531
car_eval_34
12:1
1728
21
isolet
12:1
7797
617
crime
12:1
1994
100
thyroid
15:1
3772
solar flare.m0
19:1
1389
32
22:1
937
car_eval_4
26:1
1728
21
wine_quality
26:1
4898
11
letter img
26:1
20000
16
yeast me2
28:1
1484
8
mammography
42:1
11183
6
in the sample space through Eq. and Eq.2. and then select the M samples with the lowest fitness. M is set to Nº N2 in this article. After performing crossover and mutation operations on candidate samples, 3M new samples Pr will be obtained, and Nayn samples with the highest fitness will be selected from PUP" P' as the basic population for the next iteration.
Algorithm ROGA
Require: X:all of original samples; Y:all of labels; K'number of neighbors; Gaussian kernel variance; T:tteration number;
Ensure: P: synthetic samples
1: N syn =Number of synthetic samples
2:
(ampllnnSn 3: for i [1 do
4: for x E PPde do
5: Find K nearest neighbors, and compute fitness by Eq. and Eq.
6: end for
7:
Candidates=select M samples with the lowest fitness from P
8:
SymSumplessraee
PenessoverOperatiiin (andidates)
9:
Samplem mutaatin
M ulation Candidates)
10:
P' = -ym Sampleeseeroe U SynSamppl campplessmene es
U pdate( PO)
12:
end for
4 EXPERIMENT
4.1 DATASET
The datasets come from UCI database(Dua & Graff (2017)), and the detailed informations of the datasets are shown in Table The classification goals of all datasets are binary classification prob- lems. If the goal of the dataset is multi- culti-classification problem, certain class will be set as the target class, and the remaining categories will be divided into non-target classes and converted into binary classification problem. Each dataset will be divided into 80% training set and 20% test set.
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4.2 EVOLUTION
For the binary classification problem, a confusion matrix can be obtained on the results, as shown in [ TP represents the number of positive classes predicted in the positive class, FP represents the number of predicted positive classes in the negative class, FN represents the number of negative classes predicted the positive class, and TN represents the number of negative class predicted to be negative class. The confusion matrix can be used to calculate the accuracy, precision, recall and Fl-score.
Table 2: Confusion Matrix
Predicted Positive
Predicted Negative
Positive Negative
TP
FN TN
FP
Accuracy can be defined as:
TP TN TP TNN + +FP FP+ FN
Accuracy
(6)
Precision can be defined as: Recall can be defined as: F1 -score can be defined as:
TP
Precision
TP + FP
TP TP + FN
Recall
(8)
Recall Precision
Fl = Recall + Precision
(9)
4.3 PERFORMANCE
This paper uses six oversampling algorithms to compare with ROGA, including Baseline, SMOTE/Chawla et al. (2002)). MoOdel-orderl (Nguyen et al. (201T). ADASYM(He et al. (2008), ) KMeans- KMeans-SMOTE SMOTE(Douzas Bacao (2018)), and SMOTENC Chawla et al. (202 Base- line represents the unused oversampling algorithm. The experimental results
CIOOTENCCChwla
shown in Table Due to the large fluctuations in the experimental results of ROGA, the experimental results ROGA the best results among the 20 experiments. The evaluation method used in the table is F1-score, and the classification algorithm uses XGBoost(Chen & Guestrin (2016)). For each experiment, the hyperparameters of classification algorithm are fixed. The remaining metrics will be presented in the appendix. represents the best score in this dataset.
From Table 3 can be found that ROGA has achieved the best results on most datasets, and even the improvement effect is more obvious, such as ecoil. ROGA and other oversampling algorithms both measure noise the distribution to remove noise, and ROGA with a wider range generates samples that are more conducive to model training.
At the same time, also found that artificial samples generated by the oversampling algorithm on some datasets lead to lower scores on the test set. This shows that the generation space constrained by the oversampling algorithm is not suitable for the distribution of all datasets, which can only balance the number of categories and cannot provide benefits to the training of the model. The limited generation range also causes the oversampling algorithm to be unable to deviate from the current local optimal solution. In order avoid this kind of solidification, ROGA will perform random sampling in the entire sample space before first iteration. This random sampling can make ROGA deviate from the current local optimum and achieve better results. From the results the isolet dataset, it can be seen that the oversampling algorithms as a comparison are lower than the baseline in the test set score, while ROGA improves the effect of the model.

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Table 3: Performace
Name
Base
SMOTE
Borderlinel
Adasyn
Kmeans
NC
ROGA
Ecoil
0.7873
0.8541
0.8205
0.7662
0.7873
0.7873
0.94137
optical_digitt
0.9688
0.9628
0,9633
0.9718
0.9641
0,9669
0,9737
Satimage
0.85421
0.8311
0.8379
0.8352
0.8503
0.8405
0.8455
pen_digits
0.9855
0.9886
0.9897
0.9876
0.9887
0.9855
0.98971
sick euthyroid
0.9462
0.9413
0,9493
0.95391
0.9462
0,9377
0,9493
spectrometer
0.8626
0.8969
0.8334
0.8969
0.8626
0.8721
0.93821
car evale.
0.9616t
0.9057
0.8922
0.9000
0.9279
0.9057
0,9423
isolet
0.9158
0.9069
0.8952
0.9089
0.9158
0.9069
0.9266
us_crime
0.7017
0.7035
0.6848
0.7028
0.6909
0.6880
0.7448
thyroid_sick
0.9502
0.9332
0,9435
0,9300
0.9354
0.9300
0.9627t
solar flare m0
0.4867
0.5184
0.5184
0.5123
0.4844
0.5206
0.5728
oil
0.7254
0.7258
0.7112
0.7616
0.7112
0.6867
0.7864
car_eval_4
0.9616
0.9249
0.9329
0.9499
0.9576
0.9616
0.9576
wine_quality
0.5731
0.6856
0.6655
0.6918
0.6768
0.7001
0.6275
letter-img
0.9815t
0.9746
0.9792
0.9747
0.9814
0.9746
0.9771
yeast me2
0.7291
0.6895
0.7431
0.7416
0.6806
0.7416
0.7445
mammography
0.8759
0.8175
0.8334
0.8095
0.8871
0.8643
0.9015
4.4 LIMITATION
ROGA generates the basic population through random sampling, which expands the sample genera- tion space on the one hand, but also increases uncertainty. Therefore, each artificial sample generated by ROGA fixed, and compared with other oversampling algorithms, this uncertainty is more serious.
Figure I shows that the ROGA model test results are unstable. The uncertainty contained in the initial population generation will make the model after training better, or it may not improve the model effect significantly. Therefore, when using ROGA to solve class imbalance, it necessary to conduct many experiments possible to find the artificial samples that perform best on specific metric. However, under the premise that the influence of artificial samples cannot be well described, the traditional oversampling paradigm that try to compensate for the impact of class imbalance through an experiment is unrealistic. Multiple sampling and evaluation are more conducive to taking advantage of oversampling.
CONCLUSION
In order to avoid the generation of noise, the current oversampling algorithm performs sampling in limited generation space. However, the limited generation space will cause the oversampling algorithm to fall into local optimal solution, so that it cannot effectively generate artificial samples that are beneficial to model learning. In order balance the extensive generation space and noise generation, this paper proposes the ROGA algorithm. Use random sampling to generate the initial sample population to ensure sample generation space. Then, new artificial samples are continuously generated through genetic algorithm, and noise points in the population are eliminated according to fitness. Experiments have proved that ROGA has achieved the best F1-score on multiple datasets. Therefore, the wide generation space is conducive to the oversampling algorithm to collect more high-quality samples, which can not only balance the difference quantity but also benefit the model learning.

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0330
(a) ecoli
(b) optical digits
0.6
0.00
satimage
(d) sick_euthyroid
Figure 1: Fl-score in 20 experiments. This paper chooses four datasets to preform the uncertainty of ROGA. test. id represents the experiment index and vertical axis represents the F1-score. The value range of the vertical axis of each graph is not the same. Although the amplitude is similar, does mean that the range of change the same.
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William A Rivera, Amit Goel, and J Peter Kincaid. Oups: A combined approach using smote and propensity score matching. pp. 424-427, 2014.
Jerzy Stefanowski and Szymon Wilk. Selective pre-processing of imbalanced data for improving classification performance. pp. 283-292, 2008
Fredy Rodriguez Torres, Jesus Ariel Carrascoochoa, and Jose Fco Martineztrinidad. Smote-d a deterministic version of smote. pp. 177-188, 2016.
Nele Verbiest, Enislay Ramentol, Chris Cornelis, and Francisco Herrera. Improving smote with fuzzy rough prototype selection to detect noise in imbalanced classification data. pp. 169-178, 2012.
Wang, Xu, Wang, and Zhang. Classification of imbalanced data by using the smote algorithm and locally linear embedding. In International Conference on Signal Processing, 2007.
Shuo Wang and Xin Yao. Diversity analysis on imbalanced data sets by using ensemble models. In IEEE Symposium Computational Intelligence and Data Mining, 2009.
Fan Salvatore J. Stolfo Wei. Adacost: Misclassification cost-sensitive boosting. 1999.
Alden H. Wright. Genetic algorithms for real parameter gptimization. In Proceedings of the First Workshop on Foundations of Genetic Algorithms. Bloomington Campus, Indiana, USA, July 15- 18 1990, 1990.
Tianlun Zhang and Xi Yang. G-smote: gmm-based synthetic minority oversampling technique for imbalanced learning. arXiv: Learning, 2018.
10

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Under review as a conference paper at ICLR 2021
A
APPENDIX
Table 4: Precision
Name
Base
SMOTE
Borderlinel
Adasyn
Kmeans
NC
ROGA
ecoil
0.7873
0.8269
0.7792
0.7156
0.7873
0.7873
0.9938t
Seticalddggit
0.9869t
0.9713
0,9650
0.9769
0.9859
0.9792
0.9842
Satimage
0.88661
0.8086
0.8226
0.8112
0.8824
0.8207
0.8802
pen_digits
0.9955
0.9961
0.9963
0.9942
0.9944
0.9955
0.9946
sick-euthyme
0.9717
0.9506
0.9779
0.9686
0.9717
0.9494
0.9779
spectrometer
0.9758
0.9418
0.9720
0.9418
0.9758
0.9341
0.9876
car eval 34
0.8812
0.8604
0.8485
0.8529
0.8999
0.8604
0.9134
Isolet
0.9608
0.9043
0.9018
0.9142
0.9608t
0.9043
0.9532
us us_crime 0.8256
0.6898
0.6819
0.6996
0.7793
0.6679
0.8743
thyroid_sich
0.9739
0.9217
0.9317
0.9085
0.9440
0.9085
0.9672
solar flarr-flrr_mo0
0,4755
0.5263
0,5263
0.5146
0,4753
0.5320
0.64471
oil car_eval_4 4
0.7390
0.6994
0.7112
0.7240
0.7112
0.6707
0.8037
0.9512
0.9067
0.9337
0.9306
0.9964
0.9512
0.9964
wine quality
0.6856
0.6581
0.6471
0.6613
0.6471
0.6778
0.7785
letter.img
0.9958
0.9733
0.9765
0.9709
0.9988t
0.9733
0.9868
yeast_me2
0.7431
0.6506
0.7217
0.6958
0.7060
0.6958
0.7788
mammography
0.9337
0.7693
0.7917
0.7529
0.9365
0.8400
0.9479t
Table 5: Recall
Name
Base
SMOTE
Borderlinel
Adasyn
Kmeans
NC
ROGA
Ecoil
0.7873
0.8873
0.8810
0.8684
0.7873
0.7873
0.9000
optical digits
0.9523
0.9546
0.9617
0,9668
0.9445
0.9555
0.96371
Satimage
0.8279
0.8587
0.8554
0.8651
0.8244
0.8640
0.818
pen digits
0.9760
0.9815
0.9833
0,9813
0.9831
0.9760
0.9849t
sick_ sick_euthyroid
0.9240
0.9325
0.9247
0.9403t
0.9240
0.9268
0.9247
spectrometer
0,8000
0,8624
0.7667
0.8624
0,8000
0.8291
0,9000
car_eval.34
0.9588
0.9693
0,9538
0,9681
0.9614
0.9693
0.9769t
isolet
0.8800
0.9095
0.8889
0.9036
0.8800
0.9095t
0.9035
crime
0.6509
0.7201
0.6877
0.7061
0.6488
0.7158
0.6865
thyroid_sict
0.9289
0.9455
0.9561
0,9544
0.9272
0.9544
0.9583f
solar_ftare_mo
0,4985
0.5158
0.5158
0,5113
0.4940
0.5173
0.5528t
oil
0.7134
0.7623
0.7112
0.8178
0.7112
0.7067
0.7711
car car_eval.4 4
0.9725
0.9451
0.9226
0.9714
0,9250
0.9726
0,9250
wine_quality
0.5488
0.7276
0.6904
0.7399t
0.7259
0.7301
0.5876
letter img
0.9681
0.9759
0.9818t
0.9787
0.9653
0.9759
0.9678
yeast_me2
0.7167
0.7626
0.7695
0.8209
0.6611
0.8209t
0.7181
mammography
0.8322
0.8891
0.8904
0.9036
0.8481
0.8926
0.8642
11

---

Under review as a conference paper at ICLR 2021
Table 6: Accuracy
Name
Base
SMOTE
Borderlinel
Adasyn
Kmeans
NC
ROGA
Ecoil
0.9524
0.9643
0.9524
0.9286
0.9524
0.9524
0.98811
optical
0.9900
0.9879
0,9879
0.9907
0.9886
0.9893
0.9915
Satimage
0.9528
0.9360
0.9403
0.9372
0.95121
0.9403
0.9503
pen_digits
0.9949
0.9960
0.9964
0.9956
0.9960
0.9949
0.9964
sick euthyroid
0.9798
0.9772
0,9810
0,9823
0.9798
0.9760
0.9810
spectrometer
0.9549
0.9624
0.9474
0,9624
0.9549
0.9549
0.9774
car eval.vll..
0.9154
0.9676
0,9630
0.9653
0.9769
0.9676
0.98151
isolet
0.9790
0.9744
0.9718
0.9754
0.9790
0.9744
0.9810
us_crime
0.9519
0.9299
0,9299
0.9339
0.9479
0.9218
0.9579
thyroid_sick
0.9905
0.9862
0.9883
0,9852
0.9873
0.9852
0.9926
solar flare m0
0.9482
0.9282
0,9282
0.9195
0.9396
0.9310
0.9454
oil
0.7617
0.9532
0.9574
0.9574
0.9574
0.9489
0.9702
car_eval_4
0.9931
0.9861
0.9884
0.9907
0.9931
0.9931
0.9931
wine_quality
0.9665
0.9527
0.9527
0.9527
0.9494
0.9576
0.9698
letter_img
0.9976
0.9966
0.9972
0.9966
0.9976
0.9966
0.9970
yeast me2
0.9757
0,9596
0,9730
0.9677
0.9730
0.9677
0.9784
mammography
0.9903
0.9803
0.9828
0.9782
0.9911
0.9871
0.9921
12


Reviews:
[{"review_id": "zsu2zpJ22ub", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The authors focus on the class imbalance problem and propose an algorithm named ROGA to generated samples of minority classes to balance the quantitative difference between classes. Specifically, the proposed ROGA generates samples of minority classes by using a Genetic Algorithm (GA) to explore the sample space. Moreover, to reduce the noise samples generated by ROGA, the authors propose to calculate the fitness as the Gaussian similarity with the surrounding samples and eliminate the samples with low fitness. My detailed comments are as follows.\n\nPositive points:\n1. The authors propose an algorithm named ROGA to generate samples of minority classes to balance the quantitative difference between classes.\n\n2. To reduce the noise samples, the authors propose to calculate the fitness as the Gaussian similarity with the surrounding samples and eliminate the samples with low fitness. \n\n3. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method.\n\nNegative points:\n1. The authors argue that the generation space of the proposed method is widened mainly due to the initial random sampling over the entire sample space. However, during the optimization process of GA, the sample space may be rapidly narrowed down, which makes the generation space much smaller. How to demonstrate that ROGA can ensure the sample generation space is as wide as possible during the optimization? More description and experimental analysis are required.\n\n2. The experiments are insufficient in Section 4.3. In Table 3, the compared methods are SMOTE [1] and its extensions, which are not up-to-date. It would be better to compare ROGA with more closely related and up-to-date methods such as OHEM [2], Focal Loss [3], and SMOTE-WENN[4].\n\n3. It is not clear why the samples with low fitness are selected to perform the crossover and mutation operation. Does the algorithm converge effectively under this setting?\n\n4. The reason behind the choice of the mutation operation is not clear. Why the feature value can only be positively scaled?\n\n5. Lots of experimental details such as hyper-parameters $p$, $\\beta$, training iterations, and the probability of crossover/mutation are missing, which makes the paper hard to be reproduced.\n\n6. In Table 3, why the performance of the proposed method on the wine_quality data set is much poorer than others. More explanations are required.\n\n7. The ablation study is insufficient. $K$ plays an important role in sample generation. More results on different values of $K$ are required.\n\nMinor issues:\n1. In Section 2.2, “RACOG and WRACOG (Das et al. (2015)) used the dependency tree algorithm …” should be “RACOG and WRACOG (Das et al. (2015)) use the dependency tree algorithm …”.\n\n2. In the Introduction, “oversampling methods only can estimated …” should be “oversampling methods only can estimate …”.\n\n3. In Section 4.4, “traditional oversampling paradigm that try to …” should be “traditional oversampling paradigm that tries to …”.\n\nReferences:\n\n[1] Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(1):321–357, 2002.\n\n[2] Shrivastava, Abhinav & Mulam, Harikrishna & Girshick, Ross. (2016). Training Region-Based Object Detectors with Online Hard Example Mining. 761-769. 10.1109/CVPR.2016.89. \n\n[3] Lin, Tsung-Yi & Goyal, Priyal & Girshick, Ross & He, Kaiming & Dollar, Piotr. (2018). Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. PP. 1-1. 10.1109/TPAMI.2018.2858826. \n\n[4] Guan H, Zhang Y, Xian M, et al. SMOTE-WENN: Solving class imbalance and small sample problems by oversampling and distance scaling[J]. Applied Intelligence, 2020: 1-16.\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "DIqx0CBv6u", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The authors tackle the problem of class imbalance in supervised learning. They propose an oversampling algorithm that is based on a genetic algorithm. Samples are iteratively generated and filtered to maintain informative samples. The authors demonstrate the efficacy of the approach using several examples.\n\nAlthough the problem is important and the solution might be interesting, I feel that the paper is not scientifically sound and the English level is not satisfactory. Below I will detail some of the problems with the current version of the manuscript:\n\n-First paragraph: “only can estimated” - this is is not in English.\n\n-First paragraph: “Ensure that the sample does not make…” - again this sentence does not make sense.\n\n-Authors did not use the correct citation format, please see instructions by ICLR.\n\n-P2: SMOTEChawla - here the citation is mixed with the method name.\n\n-P3: Holland - the year is mentioned twice.\n\n-2.3 has the title “Genetic Algorithm” and 3.1 has the title “GA”, aren’t these the same???\n\n-3.1 the paper does not generate anything, perhaps you meant the method?\n\n-Equation (2)  x_ik is not defined, what are the two indexes for? Sample and feature? Need to define.\n\n-Equation (2) you take the square of the square root, these cancel each other!\n\n-The authors mostly cite SMOTE based method for class imbalance, but there are other methods such as:\n\n[1] Lindenbaum, Ofir, Jay Stanley, Guy Wolf, and Smita Krishnaswamy. \"Geometry based data generation.\" In Advances in Neural Information Processing Systems, pp. 1400-1411. 2018.\n\n-Perhaps you could use bar plots for figure 1 instead of the scatter plots which are not that informative.\n\nThere are many other mistakes, so I feel that at this stage the paper can not be published at this venue.\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "BX_fFQenzjj", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "In this work, the authors propose an oversampling technique which creates a population of synthetic samples for an imbalanced dataset using genetic algorithms. Each individual in the GA population corresponds to a synthetic sample, and the fitness function is based on the similarity (in feature space) of the synthetic sample compared to nearby samples of the same and different classes; standard crossover and mutation operators are used. In a limited set of experiments, the proposed approach sometimes outperforms competing approaches.\n\nI think the basic idea of using a GA to avoid local minima in the feature space is reasonable, and the proposed fitness function makes sense, though it is perhaps a bit obvious. However, I believe the paper has several key limitations that need to be addressed.\n\nThe discussion on related work omits a large number of highly related references. In particular, a large number of existing papers propose to use genetic algorithms for oversampling, including: [Saladi and Dash, Soft Computing for Problem Solving, 2019], [Tallo and Musdholifah, ICST, 2018], [Ha and Lee, IMCOM 2016], and [Qiong et al., JDIM 2016]. While the proposed approach likely differs from these in some respects, the complete lack of context with respect to existing GA approaches for addressing class imbalance makes it difficult to judge the methodological novelty.\n\nSecond, the set of baselines considered in the work is rather limited. Thus, it is not clear if the paper meaningfully improves empirical performance compared to state of the art. In addition to lacking any other GA baselines, the experiments also lack a representative generative adversarial network-based approach. Considering the prevalence of such approaches, at least a standard implementation should be included in the comparison.\n\nThird, the experimental design itself could be significantly improved. The current design does not allow for any estimate of variance in performance of the methods on each dataset. Thus, it is not possible to tell if any of the results are significant. Indeed, additional experiments suggest that the proposed approach is not stable, so that further calls into question the empirical advantage of this approach. A simple way to estimate such variances could be to perform cross-validation by bootstrapping the dataset (e.g., k-fold cross-validation). It is also not clear how the hyperparameters for the other baseline methods are selected.\n\nIt is also not clear how the approach may work for datasets with non-numeric features, such as text or images. Presumably, some standard embedding method (word2vec, CNNs, etc.) could be used to first embed the data to a vector space, but it is then not obvious to me how well the proposed GA approach would be in exploring that space.\n\nMinor comments\n---------------------\n\nThe paper has numerous small grammatical errors. I do not believe these affect understanding the paper, but it requires another round of proofreading.\n\nThere is some issue with the formatting of the references in the text; they are all followed by two closing parentheses.\n\nFor the crossover operation, is \\alpha drawn uniformly from [0,1], or from a truncated Gaussian with a mean around 0.5? or something else?\n\nIn Algorithm 1, \\alpha is used for the Gaussian kernel variance, while \\sigma is used in the text.\n\nFor Figure 1, something like a box plot or violin plot is probably more appropriate for presenting that data.\n\nThe references are not consistently formatted.\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "5lAOmgLxN6n", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper introduces ROGA - an oversampling method that uses a genetic algorithm to generate synthetic samples for the minority class. Results in classification scenarios with imbalance dataset demonstrate the effectiveness of the proposed method.\n\nA lot of grammatical errors mar the readability of the manuscript. Resolving them through proofreading and copy-editing would improve the clarity of the paper. \n\nThe related work section is extensive and provides a good overview of related literature on methods that address class imbalances. However, the whole section reads like a laundry list of previous methods instead of painting a cohesive story about what has been done, what gaps remain and most importantly how the proposed method (ROGA) can address these gaps. It is difficult to place where ROGA fits in amongst previous literature.\n\nSection 4.2 (definitions for accuracy, precision, recall, F1, TP, FP) is background and does not belong in the method section. I would suggest that these could be removed entirely from the paper as this are basic machine learning terms that are well known and understood.  \n\nThe presented results seem to be the max score achieved across 20 independent experiments. Is this correct? If so, this will have high bias. Why not report the first order statistics based on the 20 experiments? For example, the mean score achieved, and standard deviation would enable the reader to have a much more balanced view of the method’s merits and limitation.\nFurther, it seems this max selection was not used for the other baselines in the comparative results. This would raise significant questions regarding the validity of the comparative results.\n\nWhat is the added computational cost of ROGA versus a traditional oversampling method that does not use a population? Since the fitness uses K-nearest neighbor Gaussian similarity, the computation overhead can be substantial depending on how many iterations must be run. This should be reported as it is an important consideration in large scale deployments. \n\nThe novelty in the method is marginal. The principal contribution of the method is an attempt to leverage the diversity benefits of a population-based method (GA in this case) towards generating higher quality synthetic samples for oversampling. However, the results as they stand do not fully corroborate that this benefit translates effectively to improved performance. \n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes to address the class imbalance problem by defining an over-sampling strategy based on oversampling. It brings potentially interesting ideas. The reviewers agree on the fact that the experiments are limited, some methodological aspects require some clarifications and the writing needs to be improved. 
The authors did not provide any rebuttal.
Hence I recommend rejection.

Decision: Reject