Complexity
Volume 2021 (2021), Article ID 6877284, 9 pages
https://doi.org/10.1155/2021/6877284

Research Article

HSDP: A Hybrid Sampling Method for Imbalanced Big Data Based on Data Partition

Liping Chen, Jiabao Jiang, and Yong Zhang

School of Information Engineering, Chaohu University, Chaohu, China

Correspondence should be addressed to Liping Chen; clp_luck@163.com

Received 30 April 2021; Accepted 6 June 2021; Published 22 June 2021

Academic Editor: Huihua Chen

Copyright © 2021 Liping Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The classical classifiers are ineffective in dealing with the problem of imbalanced big dataset classification. Resampling the datasets and balancing samples distribution before training the classifier is one of the most popular approaches to resolve this problem. An effective and simple hybrid sampling method based on data partition (HSDP) is proposed in this paper. First, all the data samples are partitioned into different data regions. Then, the data samples in the noise minority samples region are removed and the samples in the boundary minority samples region are selected as oversampling seeds to generate the synthetic samples. Finally, a weighted oversampling process is conducted considering the generation of synthetic samples in the same cluster of the oversampling seed. The weight of each selected minority class sample is computed by the ratio between the proportion of majority class in the neighbors of this selected sample and the sum of all these proportions. Generation of synthetic samples in the same cluster of the oversampling seed guarantees new synthetic samples located inside the minority class area. Experiments conducted on eight datasets show that the proposed method, HSDP, is better than or comparable with the typical sampling methods for F-measure and G-mean.

1. Introduction

In the era of big data, tremendous amount of data generated by various real-world applications brings the challenges to data mining. Among the challenges, classification of imbalanced datasets has drawn interest in various application areas. A dataset is imbalanced when the number of samples in one category is much less than the number of samples in other categories. If the samples come from two classes, the data of the larger number is called the majority class, and the data of the smaller number is called the minority class. Our research focuses on the binary classification problem (the two-class classification) and the prediction of minority samples is more important, because the cost of misclassification for minority samples is greater than the cost of misclassification for majority samples. The issue of binary classification of imbalanced data exists in various applications, such as medical diagnosis.

Most classifiers aim at maximizing the overall classification accuracy of a dataset. Therefore, when classifying imbalanced data, the classifier is biased to meet the classification accuracy of the majority samples, causing low classification accuracy over the minority class. In addition, an imbalanced dataset combination with other difficulty factors such as class overlapping, presence of outliers, and small disjunctions will be more difficult for the classifier to predict minority class [1]. Figure 1(a) shows the skewness distribution between classes. Figure 1(b) shows class overlapping, and Figure 1(c) shows the small disjunctions of minority class. Therefore, how to improve the classification accuracy of minority samples while ensuring the overall classification performance of the classifier for imbalance data is an urgent problem to be solved.

(a)

(b)

(c)

Figure 1: Imbalanced date distribution. (a) Skewness distribution. (b) Class overlapping. (c) Small disjunctions.

The remainder of this paper is organized as follows. Section 2 presents related works. Section 3 describes the proposed HSDP method. Section 4 introduces the experimental settings. Section 5 presents the experimental results and compares our approach with some typical techniques. Finally, the conclusion is drawn in Section 6.

2. Related Works

The techniques proposed to improve classification for imbalanced data can be categorized into two major groups: data-level methods and algorithm-level methods. The algorithm-level methods modify the classifier in order to improve the accuracy of imbalanced data. The algorithm-level methods mainly include cost-sensitive methods and ensemble learning methods. The data-level methods mainly include undersampling for the majority class [2], oversampling for the minority class. In contrast, the data-level methods are conducive to enhancing the generalization ability of the model, and the oversampling methods have more advantages because they do not lose the data sample information [3, 4]. SMOTE is the one of most popular oversampling algorithms [5]. SMOTE first selects a random seed x from the minority samples and then randomly selects sample y among its k neighbors in the same class, Finally, a new synthetic sample s is generated by the linear interpolation. This can be expressed as

where gap is a random number between 0 and 1.

Although the SMOTE algorithm has shown successful performance in various classification scenes, the SMOTE algorithm also has some weaknesses: (1) if the noisy sample is selected, oversampling process may generate more noisy samples. (2) It does not consider the data distribution when generating the synthetic sample, thereby increasing the overlaps between different classes [6]. (3) It oversamples uninformative minority samples because it chooses a minority sample seed to oversample with uniform probability. However, those minority samples on the boundary area contain more information than ones far from the boundary [7]. Therefore, researchers have proposed some improved versions of SMOTE. The Borderline-SMOTE algorithm [8] oversamples the borderline minority samples. However, sometimes the Borderline-SMOTE generates new synthetic samples in unsuitable areas, such as noise regions and overlapped areas. ADASYN algorithm [9] pays more attention to the minority samples that are difficult to learn. It can adaptively generate minority samples according to the ratio of samples of majority class in the neighborhood samples. The K-means-SMOTE algorithm [10] combats between-class imbalance and within-class imbalance. But it does not provide a strategy for determining the optimal number of clusters k, which has a great impact on the performance of oversampling. The MWMOTE technique [11] analyzes the hard-to-learn minority samples and assign them weights according to their importance in learning.

In summary, the methods above have mitigated some of the problems of SMOTE, but neither of them has effectively solved all the three problems. So, the proposed hybrid sampling method based on data partition attempts to overcome all three problems. It is able to select proper minority samples for oversampling and improve the synthetic sample generation scheme. The generation scheme includes the size of synthetic samples for selected minority samples and the control of the location of the generated samples in data space.

3. Proposed Method

3.1. Overview

The data samples present different distribution characteristics in data space, and the data distribution can be considered when undersampling or oversampling. Different sampling methods are used in different regions that may improve classification performance, and we propose the hybrid sampling method of imbalanced data based on data partitioning (HSDP). The method consists of four stages: (1) partitioning space of the input imbalanced data into five regions; (2) removing the samples in the noise minority samples region; (3) using agglomerative hierarchical clustering method to cluster the minority samples; (4) oversampling process. In the first stage, the data space is divided into five regions: the boundary minority samples region, the noise minority samples region, the safe minority samples region, the boundary majority samples region, and the safe majority samples region. And the first two stages are performed because our aim is to oversample the borderline minority samples while ignoring the noisy minority samples. The basic idea is that the borderline samples are apt to be misclassified. In the third stage, clustering the minority samples is to ensure that the generated samples must be inside the minority class regions. In the fourth stage, the oversampling process is performed, which adaptively generates synthetic samples for borderline minority samples in the same cluster of the oversampling seed. The oversampling process in the same cluster ensures that the generated samples locate inside the minority class regions.

3.2. Data Partition

According to the proportion of minority samples in neighborhoods of each minority sample, the data space is divided into five regions [12]: the boundary minority samples region, the noise minority samples region, the safe minority samples region, the boundary majority samples region, and the safe majority samples region, as shown in Figure 2.

Figure 2: Data space partition.

Given an imbalanced training dataset S and minority class label class (min), the training dataset is divided into majority class set and minority class set firstly. Then, for each sample xi in the minority class set , we calculate k neighbors around xi through the K-nearest neighbor algorithm. Next, in these k neighbors, the number of the minority class samples is computed and the majority class samples are put into the boundary majority samples region . Finally, by judging , each sample is added to corresponding region. If , the minority class sample is added to the noise minority samples region . If , the minority class sample is added to the safe minority samples region . If , the minority class sample is added to the boundary minority samples region . The safe majority samples region and the set with the sample in removed are determined at the end. The DP algorithm for the data partitioning is described as follows (Algorithm 1):

Input: imbalanced training dataset S, the number of nearest neighbors k, Minority class label class (min)

Output: five regions: , , , , , and dataset S without the samples in

Procedure:

(1)
    forin S

(2)
        if (class () = = class (min))//label of sample is the class (min)

(3)
            //add the sample into minority class dataset

(4)
        else

(5)
            //add the sample into majority class dataset

(6)
            end if

(7)
    end for

(8)
    forin

(9)
            //find k neighbor samples of each minority class samples

(10)
            //initialize the number of minority class samples

(11)
            forin list

(12)
                if (class () = = class (min))//label of sample is the class (min)

(13)

(14)
                else

(15)
                    //add majority class sample into boundary majority samples region

(16)

(17)
                end if

(18)
            end for

(19)
            if ()

(20)
                    //add minority class sample into the safe minority samples region

(21)
            else if ()

(22)
                    //add minority class sample into the noise minority samples region

(23)
                else

(24)
                    //add minority class sample in to the boundary minority samples region

(25)
    end for

(26)
    forin

(27)
        if (!(in))

(28)
        //add majority class sample into the safe majority samples region

(29)
        end if

(30)
    end for

(31)
    for in S

(32)
        if (in)

(33)
            //delete samples in the noise minority class region and retain samples from other regions

(34)
            end if

(35)
    end for

Algorithm 1: DP Algorithm.

3.3. Clustering Minority Class Samples Based on Hierarchical Clustering

Most of the existing oversampling methods are the K-NN based approach. To generate a synthetic sample from the minority class sample B and k = 5, sample A may be chosen (as shown in Figure 3). By this way, the generation of a synthetic sample (shown by square) may locate in the majority class region.

Figure 3: Illustrating of a generated synthetic sample.

Our proposed method chooses the sample from the same cluster (Cluster1) of B. It ensures that A will not be chosen, because B and A are not in the same cluster. Thus, the oversampling process is performed in a safe range and the generation of minority samples must locate inside the minority class region.

The hierarchical clustering algorithm is used to cluster the minority class samples in this work. And, the key steps of the agglomerative hierarchical clustering algorithm are described as follows:

(1)Assign each data sample to a cluster initially.(2)Find the two closest clusters and merge them into a single cluster. And, this will reduce the total number of clusters by one.(3)Compute the distance between the newly generated cluster and all the previous clusters.(4)Repeat steps 2-3 until a certain termination condition is reached. The termination condition is the number of clusters set in advance or distance threshold.

However, using agglomerative hierarchical clustering algorithm to cluster the minority class samples, whether the two minority clusters are merged or not, not only the distance between the minority clusters but also the distribution of the majority class samples should be examined. If the distance between two minority class clusters is , the distances from a certain majority class cluster to these two minority clusters are d₁ and d₂, respectively; where and . Then, these two minority class clusters cannot been merged.

Therefore, modifications to agglomerative hierarchical clustering algorithm have been made. First, agglomerative hierarchical clustering algorithm is used to cluster the majority class samples to obtain the majority cluster set . Then, the minority class samples are clustered. The minority class cluster algorithm based on hierarchical clustering algorithm [13] (MDH) is described below (Algorithm 2).

Input: majority class cluster , minority class without samples of the noise minority samples region , threshold

Output: minority class cluster

Process:

(1)
    d = 0;//initialize the minimum distance between clusters

(2)
    for

(3)
        //initialize the minority class cluster

(4)
    end for

(5)
    while ()

(6)
        //the distance matrix between clusters

(7)
        //the minimum distance d and corresponding cluster numbers and

(8)
        for Ciin//each majority class cluster

(9)
            if () && () < d))

(10)
                //the flag of cluster mergence is not

(11)
            else

(12)
                //merge and into a single cluster

(13)

(14)
                //reduce the total number of clusters by one

(15)
            end if

(16)
        end for

(17)
    end while

Algorithm 2: MDH Algorithm.

Because the distance threshold is the termination condition of the clustering process, the setting is particularly critical. In this work, is computed as follows:

The parameter represents the average of the distance from each minority class sample to any other minority samples in the Set S. The parameter r is used to tune the output distance of the cluster algorithm. And the specific value analysis of r is discussed in Section 5.

3.4. Description of Hybrid Sampling Algorithm Based on Data Partition

We proposed a hybrid sampling algorithm based on data partition. Firstly, the boundary region can be obtained by DP algorithm. Then, the total number of synthetic samples generated in the boundary region is calculated. Next, the weight of each sample in the boundary minority samples region is computed by the ratio between the proportion of majority class in the neighbors of this selected sample and the sum of all these proportions. Finally, for each sample xi in the boundary minority samples region , synthetic samples should be generated in the same cluster of . The hybrid sampling algorithm based on data partition (HSDP) is implemented as follows (Algorithm 3):

Input: imbalanced dataset S

Output: balanced dataset S

Process:

 Step 1:, , , can be obtained by DP algorithm.

 Step 2: count the number (m) of samples in the and . Count the number (n) of samples in the and . Meanwhile, count the number (s) of samples in the .

 Step 3: calculate the number of synthetic data samples that need to be generated for minority class: , where is the synthesis scaling factor. b = 1 means a balanced dataset is obtained after the oversampling process.

 Step 4: for each sample , calculate the ratio of majority class samples belonging to the k neighbors of . This ratio is defined as

 Step 5: the weight is determined by .

 Step 6: calculate the number of synthetic data samples for each sample xi in the boundary minority samples region: .

 Step 7: for each sample xi in the boundary minority samples region, generate synthetic data samples according to the following steps:

    Do the loop from 1 to :

(a)
        Randomly select another sample y from the same cluster of xi

(b)
        Generate a synthetic data sample:

        ,

                where

    End loop

Algorithm 3: HSDP Algorithm.

3.5. The Time Complexity Analysis of HSDP Algorithm

In the DP algorithm, supposing the number of minority samples is and the number of majority samples is , each minority sample needs to calculate the distance from other samples to find neighbor samples. Therefore, the time complexity of calculating the distance between samples is

In the MDH algorithm, the distance between each minority cluster and the majority clusters needs to be calculated. Suppose that the current number of minority clusters is and the number of majority clusters is . The time complexity of calculating the distance between minority clusters is , and find the two minority clusters with the smallest distance. Then, calculate the distance between the majority cluster and the two minority clusters with the smallest distance. So, the calculation time complexity to get the distance between the majority cluster and the minority cluster is , and then determine whether to merge the two minority clusters according to the distance between the clusters, and the possible number of merger time is . Therefore, the time complexity of the MDH algorithm is .

In the HSDP algorithm, assuming that the number of boundary minority samples is , the number of minority class samples is , and the number of majority class samples is , and the time complexity the step of determining the k neighbors of boundary minority samples is . In the step of sample generation, the computational time complexity is .

According to the analysis of the above steps, the time complexity of the HSDP algorithm should be .

4. Experiment Setup

4.1. Dataset Description

We test our algorithm on datasets from various filed, including 8 imbalanced datasets. All these datasets are available from KEEL Repository and UCI Repository. Table 1 describes the information of these datasets.

Table 1: Dataset information.

Dataset
Samples
Attributes
Classes
Imbalance ratio

Pima
768
8
{Negative, positive}
1.87

Yeast3
1484
8
{Negative, positive}
8.1

Abalone19
4174
8
{Negative, positive}
129.44

Segment0
2308
19
{Negative, positive}
6.02

Page-blocks0
5472
10
{Negative, positive}
8.79

Glass5
214
9
{Negative, positive}
22.78

Ecoli4
336
7
{Negative, positive}
15.8

Haberman
306
3
{Negative: “1” other, positive: “2”}
2.78

In this study, we research the binary classification. In the two-classification problem, the majority of samples are usually also marked as negative samples, and the minority samples are also marked as positive samples.

4.2. Evaluation Metrics

For the classification problem of imbalanced data, the overall classification accuracy is not suitable for evaluation of classifiers performance, because sometimes a classification algorithm with a better overall accuracy may be at the expense of large prediction error over the minority class. Therefore, F-measure and G-mean are usually used to evaluate the performance of imbalanced classification algorithms.

F-measure and G-mean are calculated based on the confusion matrix, as shown in Table 2.

Table 2: Confusion matrix.

Predicted as positive
Predicted as negative

Actually positive
TP
FN

Actually negative
FP
TN

Based on the confusion matrix, the following equations are derived:

F-measure is computed as shown in formula (6). F-measure is the harmonic mean between the Recall and Precision. The higher F-measure can ensure that both Recall and Precision are higher, where β is a coefficient to indicate the relative importance of Recall and Precision (usually β = 1). G-mean is calculated as shown in formula (7). G-mean is the geometric mean of the minority class accuracy and majority class accuracy, and it assigns equal importance to performance of the classifier on minority class and majority class.

5. Experiment and Result Analysis

The experiment platform is Anaconda. Since the purpose is evaluating the proposed sampling method, we do not choose any special classifier; rather, we apply several of them such as KNN and RF. In order to compare the performance of our proposed hybrid sampling method (HSDP) against the other techniques, comparative experiments were carried out, including SMOTE, ADASYN, and Borderline-SMOTE.

5.1. Analysis of Experimental Results

In order to guarantee the fair comparison, the experiment uses a 10-fold cross-validation method. Tables 3–6, respectively, show the F-measure and G-mean values of various algorithms on each dataset.

Table 3: F-measure values of KNN + various sampling methods.

Dataset
Algorithm

KNN
SMOTE + KNN
ADASYN + KNN
Borderline-SMOTE + KNN
HSDP (proposed) + KNN

Pima
0.5543
0.5766
0.5824
0.5726
0.6105

Yeast3
0.6343
0.6301
0.6294
0.6528
0.6698

Abalone19
0.2019
0.3355
0.2801
0.2438
0.3045

Segment0
0.8653
0.8734
0.8697
0.8843
0.8928

Page-blocks0
0.7146
0.7301
0.7099
0.7401
0.6988

Glass5
0.7132
0.7102
0.7129
0.7265
0.6827

Ecoli4
0.5401
0.5268
0.6011
0.5701
0.6698

Haberman
0.2922
0.3027
0.3417
0.3285
0.3636

Table 4: G-means values of KNN + various sampling methods.

Dataset
Algorithm

KNN
SMOTE + KNN
ADASYN + KNN
Borderline-SMOTE + KNN
HSDP (proposed) + KNN

Pima
0.6503
0.6673
0.6721
0.6635
0.6932

Yeast3
0.7536
0.7786
0.7798
0.7824
0.8034

Abalone19
0.3165
0.6028
0.6037
0.4366
0.5437

Segment0
0.9154
0.9379
0.9440
0.9261
0.9528

Page-blocks0
0.8153
0.8629
0.8601
0.8421
0.8757

Glass5
0.7887
0.7857
0.7898
0.7890
0.7715

Ecoli4
0.7012
0.7401
0.7928
0.7429
0.8438

Haberman
0.4502
0.4810
0.5211
0.4983
0.5201

Table 5: F-measure values of RF + various sampling methods.

Dataset
Algorithm

RF
SMOTE + RF
ADASYN + RF
Borderline-SMOTE + RF
HSDP (proposed) + RF

Pima
0.5891
0.5625
0.5632
0.5710
0.6000

Yeast3
0.7326
0.7411
0.7214
0.7366
0.7611

Abalone19
0.3054
0.3123
0.3652
0.2864
0.3912

Segment0
0.9102
0.9375
0.8865
0.9292
0.9301

Page-blocks0
0.7443
0.7745
0.7586
0.7723
0.7438

Glass5
0.8148
0.7826
0.7931
0.7725
0.8201

Ecoli4
0.5701
0.5737
0.5723
0.5802
0.5623

Haberman
0.3402
0.4011
0.4386
0.4154
0.4489

Table 6: G-means values of RF + various sampling methods.

Dataset
Algorithm

RF
SMOTE + RF
ADASYN + RF
Borderline-SMOTE + RF
HSDP (proposed) + RF

Pima
0.6173
0.6412
0.6315
0.6385
0.6479

Yeast3
0.8243
0.8502
0.8473
0.8362
0.8532

Abalone19
0.2782
0.6352
0.6529
0.4564
0.7001

Segment0
0.9344
0.9501
0.9313
0.9421
0.9567

Page-blocks0
0.8348
0.8990
0.9078
0.8846
0.8815

Glass5
0.8366
0.8201
0.8302
0.8172
0.8401

Ecoli4
0.6490
0.7442
0.7751
0.7403
0.7239

Haberman
0.4659
0.5686
0.5982
0.5743
0.5991

The best results of F-measure and G-mean are bold faced on each dataset in the above tables. It is evident that the KNN and RF combined with the sampling method are better than themselves without combining sampling method in most cases, On the F-measure, the HSDP algorithm obtained 5 best results on 8 datasets, and 6 best results on G-mean value. This shows that the HSDP algorithm proposed in this paper can improve the classification effect of minority class.

Compared with the SMOTE method and the ADASYN method, (1) the HSDP method does not oversample for all minority class samples but focuses on the minority samples in the boundary area that are more important in classification and (2) the HSDP method removes the noise data, thus avoiding the noisy samples generation.

In contrast to Borderline-SMOTE, our proposed HSDP method not only considers the importance of minority class samples in boundary area but also considers the distribution characteristics of data samples, avoiding any wrong synthetic sample generation.

5.2. Analyzing the Influence of the Parameter Value Used in HSDP Algorithm

The parameters involved in the proposed method (HSDP) include the number of neighbor samples k and the distance adjustment factor r.

The value of k cannot be too small, because this will take the boundary minority class samples as noisy data and delete them by mistake.

The value of r is used to control the number of clusters. With smaller r value, the number of clusters increases and the number of samples decreases in the clusters, which will result in a decrease in diversity when synthesizing samples.

In order to determine the optimal value range of r and k, we use Pima, Glass5, and Yeast3 as the test datasets. For k value (k = 3, 5, 7, 9 and 11), the G-mean are given as shown in Table 7. For pima dataset, G-mean obtains the maximum value when k is 5. When k is 9, the glass5 achieves the maximum G-mean value. And Yeast3 achieved the maximum G-mean value when k is 7. It is evident that the value of k is appropriate in the range of 5–9. For r value (r = 0.6, 0.8, 1.0, 1.2, and 1.6), the G-mean are given as shown in Table 8. The Pima dataset achieves the maximum G-mean value when r is 0.8. Glass5 achieved the maximum G-mean value when r was 1.0. And Yeast3 achieved the maximum G-mean value when r was 1.2. It can be seen that the value of k is appropriate in the range of 0.8–1.2.

Table 7: G-mean for different k value.

k = 3
k = 5
k = 7
k = 9
k = 11

Pima
0.6015
0.6832
0.6371
0.6483
0.6144

Glass5
0.6986
0.7902
0.8209
0.8394
0.6857

Yeast3
0.8351
0.8587
0.8876
0.8129
0.7576

Table 8: G-mean for different r value.

r = 0.6
r = 0.8
r = 1.0
r = 1.2
r = 1.4

Pima
0.6254
0.6483
0.6409
0.6457
0.6347

Glass5
0.7678
0.8045
0.8246
0.7892
0.7754

Yeast3
0.7984
0.8269
0.8266
0.8548
0.8026

6. Conclusion

Data resampling method is one of the effective methods to deal with imbalanced data classification. Aiming at the problems of undersampling method and oversampling method, this paper proposes a hybrid sampling method, HSDP, based on data partition. This method uses the appropriate sampling methods for samples in different regions. And, it assigns reasonable weight to the boundary minority samples. Furthermore, it is able to oversample the selected samples inside the minority class area in the data space. The effectiveness of proposed method for the imbalanced data classification was confirmed by experiments, yet the values of the parameters used in the algorithm are selected through experiments many times. The future research direction is how to determine values of the parameters adaptively of HSDP for different datasets.

Data Availability

We use datasets from KEEL Repository and UCI Repository; our method and related parameters are provided in our paper.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This work was supported by the Natural Science Foundation of Department of Education of Anhui Province, China (no. KJ2017A452).

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