# New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification

Authors: Xiaoqing Gu; Tongguang Ni; Hongyuan Wang
Journal: The Scientific World Journal
 (2014)

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## Abstract

In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.


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## Body

## 1. Introduction

Computer techniques such as machine learning and pattern recognition have been widely adopted by modern medicine. One reason is that an enormous amount of data has to be gathered and analyzed which is very hard or even impossible without making use of computer techniques. The other reason is that computer techniques have led toward digital analysis of pathological diagnosis, automatic classification differentiating, and detecting diseases. In some cases, an early symptom of some diseases is lighter and gives no obvious pointer to a possible diagnosis; moreover, many symptoms look very similar to each other, though they are caused by different diseases. So it may be difficult even for experienced doctors to make correct diagnosis. Therefore, an automatic classification system can help doctor diagnose accurately, assess disorders remotely and evaluate the treatment process [1].In recent years, researchers have proposed a lot of approaches for medicine classification, such as neural network, Bayesian network, and support vector machine (SVM). Among them SVM is considered to be one of the most successful ones [2]. For example, to improve time and accuracy in differentiating diffuse interstitial lung disease for computer-aided quantification, a hierarchical SVM is introduced which shows promise for various real-time and online image-based classification applications in clinical fields [3]. SVM as a classifier is used for liver disorders and its correct classification rate is highly successful compared to the other results attained [4]. A two-stage approach is proposed for medical datasets classification, in which the artificial bee colony algorithm is used for feature selection and SVM is used for classification [5].The support vector machine (SVM) proposed by Vapnik [6, 7] is a novel approach for solving pattern recognition problems. SVM maps the sample points into a high-dimensional feature space to seek for an optimal separating hyperplane through maximizing the margin between two classes. In addition, SVM is a quadratic programming (QP) problem that assures that its solution is obtained once it is the global unique solution, and the sparsity of solution assures better generalization. However, most of the real-world medical datasets usually contain some outliers and noisy examples. The classical SVM is very sensitive to outliers/noise. To solve this problem, fuzzy support vector machine (FSVM) [8] is proposed, in which each sample is given a fuzzy membership that denotes the attitude of the corresponding point toward one class. The membership represents how important the sample is to the decision surface.Nevertheless, many medical datasets are composed of “normal” samples with only a small percentage of “abnormal” ones, which leads to the so-called class imbalance problems. FSM does not take into consideration the class distribution and can be sensitive to the class imbalance problem. As a result, the hyperplane of FSVM can be skewed towards the minority class, and this skewness can degrade the performance of FSVM with respect to the minority class. To tackle this problem, Veropoulos et al. [9] have proposed a method called different error costs (DEC), where the SVM objective function has been modified to assign two different misclassification cost values. It is noticed that One-Class Classification [10, 11] is sometimes used in novelty detection, and it only uses the normal training data. However, in many real medical datasets, abnormal examples exist, although they are very few. Furthermore, in classification tasks, the scatter matrix can play an important role when incorporated with local intrinsic geometry structures of samples [12]. Some methods have been recently proposed to incorporate the structure of the data distribution into SVM. A linear manifold learning method named locality preserving projection (LPP) is proposed in [13, 14], which aims at preserving the local manifold structure of the samples space. Although LPP considers enhancing the local data compactness with each manifold, it does not separate manifolds with different class labels.In this paper, we propose a new FSVM method for the class imbalance problem (FSVM-CIP) which can be used to address both the problem of class imbalance and outliers/noise. FSVM-CIP not only considers the fuzziness of each training sample but also extends manifold regularization and maximizes the localized relative margin. It takes the positive samples and negative samples into consideration with different misclassification costs according to their unbalanced distributions. We systematically evaluated the FSVM-CIP on five real-world medical datasets and compared its performance with four different SVM methods for classification. The results showed that the proposed method can improve the classification accuracy and handle the classification problems with outliers/noise and imbalanced datasets more effectively.The rest of this paper is organized as follows. Section2 briefly reviews the related works. Section 3 presents the details of FSVM-CIP in the linear case. Section 4 presents FSVM-CIP in the nonlinear case in detail. The experimental results on five medical datasets are reported in Section 5, and some concluding remarks are given in Section 6.


## 2. Related Works

### 2.1. Fuzzy Support Vector Machines (FSVMs)

In traditional SVM, all the data points are considered with equal importance and assigned the same penal parameter in its objective function. However, in many real-world classification applications, some sample points, such as the outliers or noises, may not be exactly assigned to one of these two classes, and each sample point does not have the same meaning to the decision surface. To solve this problem, the theory of fuzzy support vector machine was originally proposed in [8]. Fuzzy membership to each sample point is introduced such that different sample points can make different contributions to the construction of decision surface.Suppose the training samples are(1)S={(xi,yi,si),i=1,…,N},
where xi∈Rn is the n-dimension sample point, yi∈{-1,+1} represents its class label, and si (i=1,…,N) is a fuzzy membership which satisfies σ≤si≤1 with a sufficiently small constant σ>0. The quadratic optimization problem for classification is considered as follows:
(2)min⁡w,s,ξ12wTw+C∑i-1lsiξis.t.nyi(wTxi+b)≥1-ξi,ξi≥0,i=1,…,l,
where w is a normal vector of the separating hyperplane, b is a bias term, and C is a parameter which has to be determined beforehand to control the tradeoff between the classification margin and the cost of misclassification error. Since si is the attitude of the corresponding point xi towards one class and the slack variables ξi are a measure of error, then the term siξi can be considered a measure of error with different weights. It is noted that the bigger the si is, the more importantly the corresponding point is treated; the smaller the si is, the less importantly the corresponding point is treated; thus, different input points can make different contributions to the learning of decision surface. Therefore, FSVM can find a more robust hyperplane by maximizing the margin by letting some misclassification of less important points.In order to solve the FSM optimal problem, (2) is transformed into the following dual problem by introducing Lagrangian multipliers αi:
(3)max⁡α∑i=1Nαi-12∑i=1N∑j=1Nαiαjyiyjxixjs.t.i∑i=1Nyiαi=0,0≤αi≤siC,i=1,…,N.Compared with the standard SVM, the above statement only has a little difference, which is the upper bound of the values ofαi. By solving this dual problem in (3) for optimal αi, w and b can be recovered in the same way as in the standard SVM.


### 2.2. Locality Preserving Projections (LPP)

Locality preserving projection (LPP) [13, 14] is a linear dimensionality reduction algorithm by feature extraction or projection. It builds an adjacency graph incorporating neighborhood information of the data set using the Laplacian graph and then computes a transformation matrix which maps the data points into a subspace. This linear transformation optimally preserves local neighborhood information in a certain sense. The representation map generated by this method can be viewed as a linear discrete approximation to a continuous map that naturally arises from the geometry of the manifold.For a setX={xi} (i∈[1,N]), let Nk(xi) denote k nearest neighbors of node i, and let G denote the adjacency graph of dataset X. Here, the ith node corresponds to the data point xi and nodes i and j are connected by an edge if node i is among the k nearest neighbors of node j or if node j is among the k nearest neighbors of node i; that is, xi∈Nk(xj) or xj∈Nk(xi). The adjacency graph G can be weighed as follows:
(4)Wij={exp⁡(-∥xi-xj∥2t)ifxi∈Nk(xj)orxj∈Nk(xi)0otherwise,
where exp⁡(-∥xi-xj∥2/t) is called the heart kernel function and t is a constant. ∥xi-xj∥ is the Euclidean distance in Rn between point i and point j. LPP tries to find the transformation vector w∈Rn by minimizing the following objective function:
(5)min⁡w≠0wTXLXTws.t.mwTXDXTw=1,
where D is a diagonal matrix whose entries are column sum of W and Dii=∑jWij normalizes each weight. L=D-W is the Laplacian matrix. The transformation vector w in the objective function in (5) is given by the minimum eigenvalue solution to the generalized eigenvalue problem. LPP preserves the intrinsic geometry and local structure of the data by minimizing the objective function.


## 2.1. Fuzzy Support Vector Machines (FSVMs)

In traditional SVM, all the data points are considered with equal importance and assigned the same penal parameter in its objective function. However, in many real-world classification applications, some sample points, such as the outliers or noises, may not be exactly assigned to one of these two classes, and each sample point does not have the same meaning to the decision surface. To solve this problem, the theory of fuzzy support vector machine was originally proposed in [8]. Fuzzy membership to each sample point is introduced such that different sample points can make different contributions to the construction of decision surface.Suppose the training samples are(1)S={(xi,yi,si),i=1,…,N},
where xi∈Rn is the n-dimension sample point, yi∈{-1,+1} represents its class label, and si (i=1,…,N) is a fuzzy membership which satisfies σ≤si≤1 with a sufficiently small constant σ>0. The quadratic optimization problem for classification is considered as follows:
(2)min⁡w,s,ξ12wTw+C∑i-1lsiξis.t.nyi(wTxi+b)≥1-ξi,ξi≥0,i=1,…,l,
where w is a normal vector of the separating hyperplane, b is a bias term, and C is a parameter which has to be determined beforehand to control the tradeoff between the classification margin and the cost of misclassification error. Since si is the attitude of the corresponding point xi towards one class and the slack variables ξi are a measure of error, then the term siξi can be considered a measure of error with different weights. It is noted that the bigger the si is, the more importantly the corresponding point is treated; the smaller the si is, the less importantly the corresponding point is treated; thus, different input points can make different contributions to the learning of decision surface. Therefore, FSVM can find a more robust hyperplane by maximizing the margin by letting some misclassification of less important points.In order to solve the FSM optimal problem, (2) is transformed into the following dual problem by introducing Lagrangian multipliers αi:
(3)max⁡α∑i=1Nαi-12∑i=1N∑j=1Nαiαjyiyjxixjs.t.i∑i=1Nyiαi=0,0≤αi≤siC,i=1,…,N.Compared with the standard SVM, the above statement only has a little difference, which is the upper bound of the values ofαi. By solving this dual problem in (3) for optimal αi, w and b can be recovered in the same way as in the standard SVM.


## 2.2. Locality Preserving Projections (LPP)

Locality preserving projection (LPP) [13, 14] is a linear dimensionality reduction algorithm by feature extraction or projection. It builds an adjacency graph incorporating neighborhood information of the data set using the Laplacian graph and then computes a transformation matrix which maps the data points into a subspace. This linear transformation optimally preserves local neighborhood information in a certain sense. The representation map generated by this method can be viewed as a linear discrete approximation to a continuous map that naturally arises from the geometry of the manifold.For a setX={xi} (i∈[1,N]), let Nk(xi) denote k nearest neighbors of node i, and let G denote the adjacency graph of dataset X. Here, the ith node corresponds to the data point xi and nodes i and j are connected by an edge if node i is among the k nearest neighbors of node j or if node j is among the k nearest neighbors of node i; that is, xi∈Nk(xj) or xj∈Nk(xi). The adjacency graph G can be weighed as follows:
(4)Wij={exp⁡(-∥xi-xj∥2t)ifxi∈Nk(xj)orxj∈Nk(xi)0otherwise,
where exp⁡(-∥xi-xj∥2/t) is called the heart kernel function and t is a constant. ∥xi-xj∥ is the Euclidean distance in Rn between point i and point j. LPP tries to find the transformation vector w∈Rn by minimizing the following objective function:
(5)min⁡w≠0wTXLXTws.t.mwTXDXTw=1,
where D is a diagonal matrix whose entries are column sum of W and Dii=∑jWij normalizes each weight. L=D-W is the Laplacian matrix. The transformation vector w in the objective function in (5) is given by the minimum eigenvalue solution to the generalized eigenvalue problem. LPP preserves the intrinsic geometry and local structure of the data by minimizing the objective function.


## 3. FSVM for the Class Imbalance Problem in the Linear Case

In this section, we first define the local within-class preserving scatter matrix in the linear case. Secondly, the optimization problem formulation of FSVM-CIP in the linear case is given. Moreover, the fuzzy membership functions for linear FSVM-CIP are defined. Finally, the algorithm of linear FSVM-CIP is summarized.


### 3.1. The Local within-Class Preserving Scatter Matrix in the Linear Case

Following the idea of [15], we build the nearest within-class neighbor graph to model intrinsic geometry and local structure of the data. The graph preserves local neighborhood information in a certain sense and it can be viewed as a linear discrete approximation to a continuous map that naturally arises from the geometry of the manifold.Considering the fact that we have a binary classification problem, one class denoted asC1 contains sample points xi with yi=1 and the other class denoted as C2 contains sample points xi with yi=-1. Set |C1|=m1 and |C2|=N-m1, and the total number of sample points is N.Definition 1.
For each dataxi, suppose its k nearest within-class neighbors set Nk(xi) and an edge is put between xj and its neighbors. The corresponding weight matrix Wij is
(6)Wij={1Diiexp⁡(-∥xi-xj∥2t)ifxi∈Nk(xj)orxj∈Nk(xi),yi=yj0otherwise,
where Dii=∑jWij normalizes each weight.Definition 2.
The local within-class preserving scatter matrix(7)Slw=∑k=12∑xi∈Ck(xi-∑xi∈N(xj)orxj∈N(xi)Wijxj)mmmmm×(xi-∑xi∈N(xj)orxj∈N(xi)Wijxj)T=∑k=12X(k)(I(k)-W(k))T(I(k)-W(k))X(k)T,
where I(k) is an Nk×Nk diagonal matrix. In this case, the obtained nearest within-class neighbor graph attempts to preserve the local structure of the data set and (I(k)-W(k))T(I(k)-W(k)) preserves locality of nearby points with same class label in the embedding space during the unfolding process of nonlinear structures [15]. In fact, a heavy penalty is applied to the objective function through the weight Wij if the neighboring data xi and xj are mapped far apart. Hence, the minimization criterion is an attempt to ensure points yi and yj close to each other as well as xi and xj being close.It is worthwhile to note that the local within-class scatter matrixSlw is symmetric and positive semidefinite. Slw looks similar to the within-class scatter matrix Sw [16, 17] and the Laplacian matrix L in LPP. However, Slw reflects the intrinsic geometry and local structure of the data, and Sw only considers the mean value of samples in different classes. Slw carries the class label information and discriminating information but L only considers the information of nearest neighbors for each data point in the input space, without considering the class labels.


### 3.2. FSVM-CIP in the Linear Case

To tackle the imbalance classification problem with noise and outliers, we integrate FSVM, the ideas of imbalance classification problem, and the local within-class preserving scatter. On one hand, as shown in Figure1, the linear classifier presented by the hyperplane is (wTx+b=0) and defines a field for majority-class examples (wTx+b>1-ξ) and another field for minority-class examples (wTx+b>-(1+ρ-ξ)) which is used to weaken the skewness towards the minority class and enhance the locality maximum margin. On the other hand, by assigning a higher misclassification cost for the minority class examples than the majority class examples, the effect of class imbalance could be reduced. In addition, to minimize the amount of misclassifications, the local within-class scatter matrix Slw is used to preserve intrinsic geometry and local structure of the data.Figure 1
The hyperplanes of linear FSVM-CIP.Due to this, we define the primal problem of FSVM-CIP as follows:(8)min⁡w,b,ρ,ξ12wTw-vρmm+1v1m1∑i=1m1μiξi+1v2m2∑j=m1+1Nμjξj+η2wTSlwws.t.mwTxi+b≥1-ξi,i=1,…,m1mmmm-(wTxj+b)≥1+ρ-ξj,j=m1+1,…,Nmmmmξk≥0,k=1,…,N,ρ≥0,
where m1, m2 denote the number of positive (normal class or majority class) and negative (abnormal class or minority class) training points, and m2=N-m1. ρ is a nonnegative number, and ρ+1 is the margin between the hyperplane and the minority class examples. η is a nonnegative regulation constant which is the tradeoff between the local within-class scatter and the margin. Variables v1,v2 are positive penalty parameters, which tune penalty cost of the training error for positive and negative training data, respectively. ξi,ξj≥0 are the slack variables, and μi, μj are fuzzy memberships for two-class examples.Obviously,wTSlww provides prior geometrical information into the penalty terms based on manifold regularization. Minimizing wTSlww means that close data originally in the same class in the input space are likely to be close in the output place. Therefore, wTSlww aims to preserve the local information of the manifold structure.It is noted that, in FSVM-CIP, we assign different fuzzy membership values for training examples to reflect their different classes of importance. We also showed that it is similar to assign different misclassification costsμi/v1m1(μj/v2m2) for different training examples. In order to reduce the effect of class imbalance, we can assign higher membership values μj or lower parameter v2 for the minority class examples, while we assign lower membership values μi or higher v1 for the majority class. That is, our proposed method would not tend to skew the separating hyperplane towards the minority class examples as the minority class examples are now assigned with a higher misclassification cost. By means of setting μi/v1m1(μj/v2m2) and extending manifold regularization, the learned optimal separating hyperplane enhances the relative maximum margin and FSVM-CIP will be less sensitive to imbalanced class problems.Then, we transform this problem into its corresponding dual problem as follows.The primal Lagrangian is(9)L(w,b,ρ,ξ,α,γ,s)=12wTw-νρ+1v1m1∑i=1m1μiξi+1v2m2∑j=m1+1Nμjξj+η2wTSlww-∑i=1m1αi(wTxi+b-1+ξi)+∑j=m1+1Nαj(wTxj+b+1+ρ-ξj)-∑i=1Nγiξi-sρ,
with Lagrangian multipliers αi≥0, γi≥0, and s≥0. The derivatives of L(w,b,ρ,ξ,α,γ,s) with respect to the primal variables using the Karush-Kuhn-Tucker (KKT) conditions should vanish. Consider
(10)∂L∂b=∑i=1Nαiyi=0,(11)∂L∂w=Iw+ηSlww-∑i=1Nαiyixi=0,(12)∂L∂ρ=-ν+∑j=m1+1Nαj-s=0,(13)∂L∂ξi=μiv1m1-αi-γi=0,i=1,…,m1,(14)∂L∂ξj=μjv2m2-αj-γj=0,j=m1+1,…,N,
where I is an N-dimensional vector of ones, and I=[1,…,1]T. We have w=(I+ηSlw)-1∑i=1Nαiyixi.Substituting (10)–(14) into (9), we obtain the dual form of the optimization problem:
(15)min⁡α12αTHαs.t.m∑i=+1m1αi=vmmm∑j=m1+1Nαj=vmmm0≤αi≤μiv1m1,i=1,…,m1mmm0≤αj≤μjv2m2,j=m1+1,…,N,
where H is a matrix with entry Hij=yiyjxiT(I+ηSlw)-1xj, and vectors α=[α1,…,αN]T.Equation (15) is a typical convex quadratic programming problem which is easy to be numerically solved. Suppose α*=[α1*,…,αN*] can be used to solve the above optimization problem, and then the optimal weight vector is
(16)w*=(I+ηSlw)-1∑i=1Nαi*yixi.Denote a training samplexi (1≤i≤N) called a support vector (SV) if the corresponding Lagrange multiplier αi>0. Denote the SV sets as SV1={xi∣0<αi≤μi/v1m1,1≤i≤m1} and SV2={xj∣0<αj≤μj/v2m2,1+m1≤j≤N} while s+ and s- denote the number of SVs in SV1 and SV2, respectively. According to KKT condition, (15) becomes equations for the input data in SV1 and SV2, respectively, with slack variables ξi and ξj being 0. Thus, the optimal thresholds b* and ρ* can be calculated. However, from the numerical perspective, it is better to take the mean value of b* and ρ* resulting from all such data. Therefore, the optimal thresholds b* and ρ* are computed by the following formula:
(17)b*=1-1s+∑xi∈SV1(w*)Txi,(18)ρ*=-1s+∑xi∈SV1(w*)Txi+1s-∑xj∈SV2(w*)Txj.As a result, the corresponding decision function of the linear FSVM-CIP will be(19)f(x)=sgn⁡(wTx+b*)=sgn⁡(∑i=1Nαi*yi(xiT(I+ηSlw)-1x)+b*).Note that, to deal with the small sample size problem,(I+ηSlw) is regularized by adding a scale multiple η of the identity matrix Slw with I before any inversion takes place. Hence, (I+ηSlw) is always nonsingular, and the inverse of (I+ηSlw) exists.Following the terminology in [18], a training sample xi (1≤i≤N) is called a margin error (ME) if the corresponding slack variable ξi>0. We give the following theorem for parameter selection later.Theorem 3.
Letm+ and m- denote the number of MEs in the positive and negative classes; s+ and s- denote the number of SVs in the positive and negative classes, respectively. Then one has
(20)μm+¯m+≤vv1m1≤μs+¯s+,(21)μm-¯m-≤vv2m2≤μs-¯s-,
where μm+¯ and μm-¯ denote the mean fuzzy membership of MEs in the positive and negative classes; μs+¯ and μs-¯ denote the mean fuzzy membership of SVs in the positive and negative classes, respectively.A proof of the above theorem can be found in Appendix.


### 3.3. Fuzzy Membership Functions in the Linear Case

In FSVM, the fuzzy membership is used to reduce the effects of outliers or noises and different fuzzy membership functions have different influences on the fuzzy algorithm. Basically, the rule to assign proper membership values to data points can depend on the relative importance of date points to their own classes. In this paper, we consider two fuzzy membership functions given in [19].Given the sequence of training points, denote the mean of positive class and negative class asx-+ and x--.Definition 4.
Theμlin⁡ is called the linear fuzzy membership and μlin⁡ can be defined as
(22)μlin⁡={1-∥xi-x-+∥(max⁡j(∥xj-x-+∥)+δ)ifyi=11-∥xi-x--∥(max⁡j(∥xj-x--∥)+δ)ifyi=-1,
where δ is a small positive value, which is used to avoid μlin⁡ becoming zero. ∥·∥ is the Euclidean distance.Definition 5.
Theμexp⁡ is called the exponential fuzzy membership and μexp⁡ can be defined as
(23)μexp⁡={21+exp⁡(λ∥xi-x-+∥)ifyi=121+exp⁡(λ∥xi-x--∥)ifyi=-1,
where parameter λ∈[0,1] determines the steepness of the decay.


### 3.4. Solution

Based on the above, we can state the approach of proposed FSVM-CIP in the linear case as Algorithm1.Algorithm 1:FSVM-CIP in the linear case.
Input:
Training samples{(xi,yi),i=1,…,N}
Testing samples{xj,j=1,…,U}
Output:
The predicted labelsyj of data {xj,j=1,…,U}
Procedure:
(1) Compute fuzzy membershipμi using (22) or (23) for the data {(xi,yi),i=1,…,N}
(2) Construct data adjacency graphG using k nearest neighbors and compute the edge weights matrix Wij with N examples
(3) Construct local within-class preserving scatter matrixSlw using (8)
(4) Choose parameterst (6); η,ν,ν1 and ν2 (8)
(5) Computeα* using (15) and b* using (17) with a QP Solver
(6) Using decision function (19) with samples xj, and output the final class labels


## 3.1. The Local within-Class Preserving Scatter Matrix in the Linear Case

Following the idea of [15], we build the nearest within-class neighbor graph to model intrinsic geometry and local structure of the data. The graph preserves local neighborhood information in a certain sense and it can be viewed as a linear discrete approximation to a continuous map that naturally arises from the geometry of the manifold.Considering the fact that we have a binary classification problem, one class denoted asC1 contains sample points xi with yi=1 and the other class denoted as C2 contains sample points xi with yi=-1. Set |C1|=m1 and |C2|=N-m1, and the total number of sample points is N.Definition 1.
For each dataxi, suppose its k nearest within-class neighbors set Nk(xi) and an edge is put between xj and its neighbors. The corresponding weight matrix Wij is
(6)Wij={1Diiexp⁡(-∥xi-xj∥2t)ifxi∈Nk(xj)orxj∈Nk(xi),yi=yj0otherwise,
where Dii=∑jWij normalizes each weight.Definition 2.
The local within-class preserving scatter matrix(7)Slw=∑k=12∑xi∈Ck(xi-∑xi∈N(xj)orxj∈N(xi)Wijxj)mmmmm×(xi-∑xi∈N(xj)orxj∈N(xi)Wijxj)T=∑k=12X(k)(I(k)-W(k))T(I(k)-W(k))X(k)T,
where I(k) is an Nk×Nk diagonal matrix. In this case, the obtained nearest within-class neighbor graph attempts to preserve the local structure of the data set and (I(k)-W(k))T(I(k)-W(k)) preserves locality of nearby points with same class label in the embedding space during the unfolding process of nonlinear structures [15]. In fact, a heavy penalty is applied to the objective function through the weight Wij if the neighboring data xi and xj are mapped far apart. Hence, the minimization criterion is an attempt to ensure points yi and yj close to each other as well as xi and xj being close.It is worthwhile to note that the local within-class scatter matrixSlw is symmetric and positive semidefinite. Slw looks similar to the within-class scatter matrix Sw [16, 17] and the Laplacian matrix L in LPP. However, Slw reflects the intrinsic geometry and local structure of the data, and Sw only considers the mean value of samples in different classes. Slw carries the class label information and discriminating information but L only considers the information of nearest neighbors for each data point in the input space, without considering the class labels.


## 3.2. FSVM-CIP in the Linear Case

To tackle the imbalance classification problem with noise and outliers, we integrate FSVM, the ideas of imbalance classification problem, and the local within-class preserving scatter. On one hand, as shown in Figure1, the linear classifier presented by the hyperplane is (wTx+b=0) and defines a field for majority-class examples (wTx+b>1-ξ) and another field for minority-class examples (wTx+b>-(1+ρ-ξ)) which is used to weaken the skewness towards the minority class and enhance the locality maximum margin. On the other hand, by assigning a higher misclassification cost for the minority class examples than the majority class examples, the effect of class imbalance could be reduced. In addition, to minimize the amount of misclassifications, the local within-class scatter matrix Slw is used to preserve intrinsic geometry and local structure of the data.Figure 1
The hyperplanes of linear FSVM-CIP.Due to this, we define the primal problem of FSVM-CIP as follows:(8)min⁡w,b,ρ,ξ12wTw-vρmm+1v1m1∑i=1m1μiξi+1v2m2∑j=m1+1Nμjξj+η2wTSlwws.t.mwTxi+b≥1-ξi,i=1,…,m1mmmm-(wTxj+b)≥1+ρ-ξj,j=m1+1,…,Nmmmmξk≥0,k=1,…,N,ρ≥0,
where m1, m2 denote the number of positive (normal class or majority class) and negative (abnormal class or minority class) training points, and m2=N-m1. ρ is a nonnegative number, and ρ+1 is the margin between the hyperplane and the minority class examples. η is a nonnegative regulation constant which is the tradeoff between the local within-class scatter and the margin. Variables v1,v2 are positive penalty parameters, which tune penalty cost of the training error for positive and negative training data, respectively. ξi,ξj≥0 are the slack variables, and μi, μj are fuzzy memberships for two-class examples.Obviously,wTSlww provides prior geometrical information into the penalty terms based on manifold regularization. Minimizing wTSlww means that close data originally in the same class in the input space are likely to be close in the output place. Therefore, wTSlww aims to preserve the local information of the manifold structure.It is noted that, in FSVM-CIP, we assign different fuzzy membership values for training examples to reflect their different classes of importance. We also showed that it is similar to assign different misclassification costsμi/v1m1(μj/v2m2) for different training examples. In order to reduce the effect of class imbalance, we can assign higher membership values μj or lower parameter v2 for the minority class examples, while we assign lower membership values μi or higher v1 for the majority class. That is, our proposed method would not tend to skew the separating hyperplane towards the minority class examples as the minority class examples are now assigned with a higher misclassification cost. By means of setting μi/v1m1(μj/v2m2) and extending manifold regularization, the learned optimal separating hyperplane enhances the relative maximum margin and FSVM-CIP will be less sensitive to imbalanced class problems.Then, we transform this problem into its corresponding dual problem as follows.The primal Lagrangian is(9)L(w,b,ρ,ξ,α,γ,s)=12wTw-νρ+1v1m1∑i=1m1μiξi+1v2m2∑j=m1+1Nμjξj+η2wTSlww-∑i=1m1αi(wTxi+b-1+ξi)+∑j=m1+1Nαj(wTxj+b+1+ρ-ξj)-∑i=1Nγiξi-sρ,
with Lagrangian multipliers αi≥0, γi≥0, and s≥0. The derivatives of L(w,b,ρ,ξ,α,γ,s) with respect to the primal variables using the Karush-Kuhn-Tucker (KKT) conditions should vanish. Consider
(10)∂L∂b=∑i=1Nαiyi=0,(11)∂L∂w=Iw+ηSlww-∑i=1Nαiyixi=0,(12)∂L∂ρ=-ν+∑j=m1+1Nαj-s=0,(13)∂L∂ξi=μiv1m1-αi-γi=0,i=1,…,m1,(14)∂L∂ξj=μjv2m2-αj-γj=0,j=m1+1,…,N,
where I is an N-dimensional vector of ones, and I=[1,…,1]T. We have w=(I+ηSlw)-1∑i=1Nαiyixi.Substituting (10)–(14) into (9), we obtain the dual form of the optimization problem:
(15)min⁡α12αTHαs.t.m∑i=+1m1αi=vmmm∑j=m1+1Nαj=vmmm0≤αi≤μiv1m1,i=1,…,m1mmm0≤αj≤μjv2m2,j=m1+1,…,N,
where H is a matrix with entry Hij=yiyjxiT(I+ηSlw)-1xj, and vectors α=[α1,…,αN]T.Equation (15) is a typical convex quadratic programming problem which is easy to be numerically solved. Suppose α*=[α1*,…,αN*] can be used to solve the above optimization problem, and then the optimal weight vector is
(16)w*=(I+ηSlw)-1∑i=1Nαi*yixi.Denote a training samplexi (1≤i≤N) called a support vector (SV) if the corresponding Lagrange multiplier αi>0. Denote the SV sets as SV1={xi∣0<αi≤μi/v1m1,1≤i≤m1} and SV2={xj∣0<αj≤μj/v2m2,1+m1≤j≤N} while s+ and s- denote the number of SVs in SV1 and SV2, respectively. According to KKT condition, (15) becomes equations for the input data in SV1 and SV2, respectively, with slack variables ξi and ξj being 0. Thus, the optimal thresholds b* and ρ* can be calculated. However, from the numerical perspective, it is better to take the mean value of b* and ρ* resulting from all such data. Therefore, the optimal thresholds b* and ρ* are computed by the following formula:
(17)b*=1-1s+∑xi∈SV1(w*)Txi,(18)ρ*=-1s+∑xi∈SV1(w*)Txi+1s-∑xj∈SV2(w*)Txj.As a result, the corresponding decision function of the linear FSVM-CIP will be(19)f(x)=sgn⁡(wTx+b*)=sgn⁡(∑i=1Nαi*yi(xiT(I+ηSlw)-1x)+b*).Note that, to deal with the small sample size problem,(I+ηSlw) is regularized by adding a scale multiple η of the identity matrix Slw with I before any inversion takes place. Hence, (I+ηSlw) is always nonsingular, and the inverse of (I+ηSlw) exists.Following the terminology in [18], a training sample xi (1≤i≤N) is called a margin error (ME) if the corresponding slack variable ξi>0. We give the following theorem for parameter selection later.Theorem 3.
Letm+ and m- denote the number of MEs in the positive and negative classes; s+ and s- denote the number of SVs in the positive and negative classes, respectively. Then one has
(20)μm+¯m+≤vv1m1≤μs+¯s+,(21)μm-¯m-≤vv2m2≤μs-¯s-,
where μm+¯ and μm-¯ denote the mean fuzzy membership of MEs in the positive and negative classes; μs+¯ and μs-¯ denote the mean fuzzy membership of SVs in the positive and negative classes, respectively.A proof of the above theorem can be found in Appendix.


## 3.3. Fuzzy Membership Functions in the Linear Case

In FSVM, the fuzzy membership is used to reduce the effects of outliers or noises and different fuzzy membership functions have different influences on the fuzzy algorithm. Basically, the rule to assign proper membership values to data points can depend on the relative importance of date points to their own classes. In this paper, we consider two fuzzy membership functions given in [19].Given the sequence of training points, denote the mean of positive class and negative class asx-+ and x--.Definition 4.
Theμlin⁡ is called the linear fuzzy membership and μlin⁡ can be defined as
(22)μlin⁡={1-∥xi-x-+∥(max⁡j(∥xj-x-+∥)+δ)ifyi=11-∥xi-x--∥(max⁡j(∥xj-x--∥)+δ)ifyi=-1,
where δ is a small positive value, which is used to avoid μlin⁡ becoming zero. ∥·∥ is the Euclidean distance.Definition 5.
Theμexp⁡ is called the exponential fuzzy membership and μexp⁡ can be defined as
(23)μexp⁡={21+exp⁡(λ∥xi-x-+∥)ifyi=121+exp⁡(λ∥xi-x--∥)ifyi=-1,
where parameter λ∈[0,1] determines the steepness of the decay.


## 3.4. Solution

Based on the above, we can state the approach of proposed FSVM-CIP in the linear case as Algorithm1.Algorithm 1:FSVM-CIP in the linear case.
Input:
Training samples{(xi,yi),i=1,…,N}
Testing samples{xj,j=1,…,U}
Output:
The predicted labelsyj of data {xj,j=1,…,U}
Procedure:
(1) Compute fuzzy membershipμi using (22) or (23) for the data {(xi,yi),i=1,…,N}
(2) Construct data adjacency graphG using k nearest neighbors and compute the edge weights matrix Wij with N examples
(3) Construct local within-class preserving scatter matrixSlw using (8)
(4) Choose parameterst (6); η,ν,ν1 and ν2 (8)
(5) Computeα* using (15) and b* using (17) with a QP Solver
(6) Using decision function (19) with samples xj, and output the final class labels


## 4. FSVM for the Class Imbalance Problem in the Nonlinear Case

In this section, we extend the local within-class preserving scatter matrix and FSVM-CIP into feature space. Moreover, the fuzzy membership functions in feature space are defined. Finally, the algorithm of kernel FSVM-CIP is summarized.


### 4.1. Kernel Extension

In order to handle nonlinear classification, the kernelization trick [20] is used to map the n-dimensional date points into an arbitrary reproducing kernel Hilbert space (RKHS) [21] via a mapping function ϕ:Rn↦H; that is, xi↦ϕ(xi). Then a linear hyperplane f(v)=αTϕ(v)+b in feature space H would correspond to a nonlinear hyperplane in the original space Rn where α,ϕ(v)∈H, v∈Rn, and b∈R.Letϕ(X) denote the date matrices in feature space H, ϕ(X)=[ϕ(x1),ϕ(x2),…,ϕ(xn)]; then the kernel function K is a matrix with entry Kij=K(xi,xj)=ϕ(xi)Tϕ(xj).Here the kernel local within-class scatter matrixSlwϕ in feature space is
(24)Slwϕ=∑k=12∑i=1Nk(ϕ(xi)-∑j=1NkWijϕkϕ(xj))mmmm×(ϕ(xi)-∑j=1NkWijϕkϕ(xj))T=K(1)(I(1)-Wϕ(1))T(I(1)-Wϕ(1))K(1)T+K(2)(I(2)-Wϕ(2))T(I(2)-Wϕ(2))K(2)T,
where I(1), I(2) are N1-order, N2-order identity matrixes, respectively. Based on the above notations, K(1), K(2) are N×m1, N×(N-m1) matrixes, respectively; thus K=[K(1),K(2)].The weight matrixesWϕ(1) and Wϕ(2) are the nonlinear version of W(1) and W(2), respectively. Wϕ(1)and Wϕ(2) could be built by Wijϕ, and the nonlinear version of Wijϕ is
(25)Wijϕ={1Diiϕexp⁡(-(Kii+Kjj-2Kij)t)ifxi∈Nk(xj)orxj∈Nk(xi),yi=yj0otherwise,
where Diiϕ=∑jWijϕ is a normalizer.Thus, the kernel FSVM-CIP can be easily achieved by solving the following quadratic problem:(26)min⁡w,b,ρ,ξ12wTw-vρ+1v1m1∑i=1m1μiξi+1v2m2∑j=m1+1Nμjξjmmmmm+η2wTSlwϕws.t.mwTϕ(xi)+b≥1-ξi,i=1,…,m1mmmmwTϕ(xj)+b≥1+ρ-ξj,j=m1+1,…,Nmmmmξk≥0,k=1,…,N,ρ≥0.Like its linear counterpart, the solution to this optimization problem can be easily found using Lagrange multipliers. By using the representer theorem,w can be given by w=∑i=1Nβiϕ(xi). We obtain the dual form of the optimization problem:
(27)min⁡α12αTMαs.t.∑i=1m1αi=vmm∑j=m1+1Nαj=vmm0≤αi≤μiv1m1,i=1,…,m1mm0≤αj≤μjv2m2,j=m1+1,…,N,
where M=YKTQ-1KY and Q=K+ηK(1)(I(1)-Wϕ(1))T(I(1)-Wϕ(1))K(1)T + ηK(2)(I(2)-Wϕ(2))T(I(2)-Wϕ(2))K(2)T. Vectors α=[α1,…,αN]T, and Y=diag⁡(y1,y2,…,yn) is a diagonal matrix.Equation (27) is a typical convex quadratic programming problem which is easy to be numerically solved. Suppose α*=[α1*,…,αN*]T can be used to solve the above optimization problem; then the optimal weight vector β*=Q-1KYα*. Therefore, the optimal thresholds b* and ρ* are computed by the following formula:
(28)b*=1-1s+∑xi∈sv1∑j=1Nβj*yjK(xi,xj),(29)ρ*=-1s+∑xi∈SV1∑j=1Nβj*yjK(xi,xj)+1s-∑xi∈SV2∑j=1Nβj*yjK(xi,xj).Finally, a more robust decision function of kernel FSVM-CIP will be(30)f(x)=sgn⁡(∑i=1Nβi*K(x,xi)+b*).Theorem 6.
The matrixM in (27) is symmetric and positive semidefinite.A proof of the above theorem can be found in Appendix.Next, we consider fuzzy membership functions in feature space.Definition 7.
Theμlin⁡ϕ is called the linear fuzzy membership in feature space and μlin⁡ϕ can be defined as
(31)μlin⁡ϕ={1-∥ϕ(xi)-ϕ(x-+)∥(max⁡j(∥ϕ(xj)-ϕ(x-+)∥)+δ)ifyi=11-∥ϕ(xi)-ϕ(x--)∥(max⁡j(∥ϕ(xj)-ϕ(x--)∥)+δ)ifyi=-1,
where δ is a small positive value. ∥·∥ is the Euclidean distance.Definition 8.
Theμexp⁡ϕ is called the exponential fuzzy membership in feature space and μexp⁡ϕ can be defined as
(32)μexp⁡ϕ={21+exp⁡(λ∥ϕ(xi)-ϕ(x-+)∥)ifyi=121+exp⁡(λ∥ϕ(xi)-ϕ(x--)∥)ifyi=-1,
where parameter λ∈[0,1] determines the steepness of the decay. Consider
(33)ϕ(x-+)=1m1∑xi∈c1ϕ(xi),ϕ(x--)=1N-m1∑xi∈c2ϕ(xi).Thus, the distance∥ϕ(xi)-ϕ(x-+)∥ can be given by
(34)∥ϕ(xi)-ϕ(x-+)∥=K(xi,xi)-2m1∑xj∈C1K(xi,xj)+1m12∑xs∈C1∑xt∈C1K(xs,xt).
Likewise, the ∥ϕ(xi)-ϕ(x--)∥ can be given in a similar manner.


### 4.2. Solution

Based on the above, we can state the approach of kernel FSVM-CIP as Algorithm2.Algorithm 2:Kernel FSVM-CIP.
Input:
training samples{(xi,yi),i=1,…,N}
Testing samples{xj,j=1,…,U}
Output:
The predicted labelsyj of data {xj,j=1,…,U}
Procedure:
(1) Choose a kernel functionK. Compute the Gram matrix Kij=K(xi,xj).
(2) Compute fuzzy membershipμiϕ using (31) or (32) for the data {(xi,yi),i=1,…,N}
(3) Construct data adjacency graphG using k nearest neighbors and compute the edge weights matrix Wijϕ with N examples
(4) Construct local within-class preserving scatter matrixSlwϕ using (24)
(5) Choose parameterst (25); η,ν,ν1 and ν2 (26)
(6) Computeα* using (27) and b* using (28) with a QP Solver
(7) Using decision function (30) with samples xj, and output the final class labels


## 4.1. Kernel Extension

In order to handle nonlinear classification, the kernelization trick [20] is used to map the n-dimensional date points into an arbitrary reproducing kernel Hilbert space (RKHS) [21] via a mapping function ϕ:Rn↦H; that is, xi↦ϕ(xi). Then a linear hyperplane f(v)=αTϕ(v)+b in feature space H would correspond to a nonlinear hyperplane in the original space Rn where α,ϕ(v)∈H, v∈Rn, and b∈R.Letϕ(X) denote the date matrices in feature space H, ϕ(X)=[ϕ(x1),ϕ(x2),…,ϕ(xn)]; then the kernel function K is a matrix with entry Kij=K(xi,xj)=ϕ(xi)Tϕ(xj).Here the kernel local within-class scatter matrixSlwϕ in feature space is
(24)Slwϕ=∑k=12∑i=1Nk(ϕ(xi)-∑j=1NkWijϕkϕ(xj))mmmm×(ϕ(xi)-∑j=1NkWijϕkϕ(xj))T=K(1)(I(1)-Wϕ(1))T(I(1)-Wϕ(1))K(1)T+K(2)(I(2)-Wϕ(2))T(I(2)-Wϕ(2))K(2)T,
where I(1), I(2) are N1-order, N2-order identity matrixes, respectively. Based on the above notations, K(1), K(2) are N×m1, N×(N-m1) matrixes, respectively; thus K=[K(1),K(2)].The weight matrixesWϕ(1) and Wϕ(2) are the nonlinear version of W(1) and W(2), respectively. Wϕ(1)and Wϕ(2) could be built by Wijϕ, and the nonlinear version of Wijϕ is
(25)Wijϕ={1Diiϕexp⁡(-(Kii+Kjj-2Kij)t)ifxi∈Nk(xj)orxj∈Nk(xi),yi=yj0otherwise,
where Diiϕ=∑jWijϕ is a normalizer.Thus, the kernel FSVM-CIP can be easily achieved by solving the following quadratic problem:(26)min⁡w,b,ρ,ξ12wTw-vρ+1v1m1∑i=1m1μiξi+1v2m2∑j=m1+1Nμjξjmmmmm+η2wTSlwϕws.t.mwTϕ(xi)+b≥1-ξi,i=1,…,m1mmmmwTϕ(xj)+b≥1+ρ-ξj,j=m1+1,…,Nmmmmξk≥0,k=1,…,N,ρ≥0.Like its linear counterpart, the solution to this optimization problem can be easily found using Lagrange multipliers. By using the representer theorem,w can be given by w=∑i=1Nβiϕ(xi). We obtain the dual form of the optimization problem:
(27)min⁡α12αTMαs.t.∑i=1m1αi=vmm∑j=m1+1Nαj=vmm0≤αi≤μiv1m1,i=1,…,m1mm0≤αj≤μjv2m2,j=m1+1,…,N,
where M=YKTQ-1KY and Q=K+ηK(1)(I(1)-Wϕ(1))T(I(1)-Wϕ(1))K(1)T + ηK(2)(I(2)-Wϕ(2))T(I(2)-Wϕ(2))K(2)T. Vectors α=[α1,…,αN]T, and Y=diag⁡(y1,y2,…,yn) is a diagonal matrix.Equation (27) is a typical convex quadratic programming problem which is easy to be numerically solved. Suppose α*=[α1*,…,αN*]T can be used to solve the above optimization problem; then the optimal weight vector β*=Q-1KYα*. Therefore, the optimal thresholds b* and ρ* are computed by the following formula:
(28)b*=1-1s+∑xi∈sv1∑j=1Nβj*yjK(xi,xj),(29)ρ*=-1s+∑xi∈SV1∑j=1Nβj*yjK(xi,xj)+1s-∑xi∈SV2∑j=1Nβj*yjK(xi,xj).Finally, a more robust decision function of kernel FSVM-CIP will be(30)f(x)=sgn⁡(∑i=1Nβi*K(x,xi)+b*).Theorem 6.
The matrixM in (27) is symmetric and positive semidefinite.A proof of the above theorem can be found in Appendix.Next, we consider fuzzy membership functions in feature space.Definition 7.
Theμlin⁡ϕ is called the linear fuzzy membership in feature space and μlin⁡ϕ can be defined as
(31)μlin⁡ϕ={1-∥ϕ(xi)-ϕ(x-+)∥(max⁡j(∥ϕ(xj)-ϕ(x-+)∥)+δ)ifyi=11-∥ϕ(xi)-ϕ(x--)∥(max⁡j(∥ϕ(xj)-ϕ(x--)∥)+δ)ifyi=-1,
where δ is a small positive value. ∥·∥ is the Euclidean distance.Definition 8.
Theμexp⁡ϕ is called the exponential fuzzy membership in feature space and μexp⁡ϕ can be defined as
(32)μexp⁡ϕ={21+exp⁡(λ∥ϕ(xi)-ϕ(x-+)∥)ifyi=121+exp⁡(λ∥ϕ(xi)-ϕ(x--)∥)ifyi=-1,
where parameter λ∈[0,1] determines the steepness of the decay. Consider
(33)ϕ(x-+)=1m1∑xi∈c1ϕ(xi),ϕ(x--)=1N-m1∑xi∈c2ϕ(xi).Thus, the distance∥ϕ(xi)-ϕ(x-+)∥ can be given by
(34)∥ϕ(xi)-ϕ(x-+)∥=K(xi,xi)-2m1∑xj∈C1K(xi,xj)+1m12∑xs∈C1∑xt∈C1K(xs,xt).
Likewise, the ∥ϕ(xi)-ϕ(x--)∥ can be given in a similar manner.


## 4.2. Solution

Based on the above, we can state the approach of kernel FSVM-CIP as Algorithm2.Algorithm 2:Kernel FSVM-CIP.
Input:
training samples{(xi,yi),i=1,…,N}
Testing samples{xj,j=1,…,U}
Output:
The predicted labelsyj of data {xj,j=1,…,U}
Procedure:
(1) Choose a kernel functionK. Compute the Gram matrix Kij=K(xi,xj).
(2) Compute fuzzy membershipμiϕ using (31) or (32) for the data {(xi,yi),i=1,…,N}
(3) Construct data adjacency graphG using k nearest neighbors and compute the edge weights matrix Wijϕ with N examples
(4) Construct local within-class preserving scatter matrixSlwϕ using (24)
(5) Choose parameterst (25); η,ν,ν1 and ν2 (26)
(6) Computeα* using (27) and b* using (28) with a QP Solver
(7) Using decision function (30) with samples xj, and output the final class labels


## 5. Experiments and Discussions

To evaluate the performance of our proposed FSVM-CIP, in this section, FSVM-CIP is evaluated compared with other related representative methods, such as standard FSVM [8], SVDD [11], FSVM for class imbalance learning (FSVM-CIL) [22], and FSVM with minimum within-class scatter (WCS-FSVM) [23]. We implement FSVM-CIP using the linear fuzzy membership and the exponential fuzzy membership, respectively, which are represented as FSVM-CIPlin⁡ and FSVM-CIPexp⁡. All the experiments are performed in Matlab (R2010a) on personal computer, whose configuration is as follows: CPU 2.99 GHz, 4.0 G RAM, and Microsoft Windows XP.


### 5.1. Data Preparation

In this section, we use five real-world medical datasets from the UCI repository of machine learning database [24], to demonstrate the classification performance of the method proposed in this paper. These five medical datasets are breast, heart, hepatitis, BUPA liver, and pima diabetes. It is highly likely that these real-world datasets contain some outliers and noisy examples in different amounts [22]. In each of them, the positive class consists of the data corresponding to the healthy, normal, or benign cases, while the negative class contains the data for diseased, abnormal, or malignant cases. Further details of these datasets are provided in Table 1. This contains the total number of positive data #pos, the total number of negative data #neg, the number of positive training examples m1, the number of negative training examples m2, the positive-to-negative imbalance ratio Ratio, and the data dimensionality d.Table 1
Characteristics of the selected datasets.
Datasets
#pos
#neg
m
1
m
2
Ratio
d
Breast
458
241
240
120
2 : 1
9
Heart
120
150
80
20
4 : 1
13
Hepatitis
123
32
100
10
10 : 1
19
BUPA liver
200
145
150
10
15 : 1
6
Pima diabetes
268
500
180
10
18 : 1
8


### 5.2. Performance Measure and Experimental Settings

We used the geometric mean of sensitivity (sensitivity = proportion of the positives correctly recognized), specificity (specificity = proportion of the negatives correctly recognized), and accuracy (accuracy = proportion of correctly classified instances) for the classifier performance evaluation in experiments, as commonly used in medical datasets classification research [7].Like the existing SVM and FSVM algorithms, the solution is sensitive to the setting of the parameters. In order to evaluate the performance, a strategy is that a set of the parameters is given first and then the best cross-validation mean rate among the set is used to estimate the generalized accuracy. We adopt this strategy in this paper. For FSVM-CIP, the parameterν is searched in {1,5,10,15,…,80}, while v1 and v2 are selected from {0.001,0.005,0.01,0.05}. η is selected from log⁡2η∈{-5,-4.5,-4,…,5.5,6}. The heat kernel parameter t is searched in {0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0} and the neighborhood parameter k is searched in {3,5,7,9,11,13,15}. In addition, when the linear fuzzy function is used, we set δ=10-6. When the exponential fuzzy function is used, the optimal value of λ is chosen from the range λ={0.1,0.2,0.3,…,1}.The regularization parameterC for FSVM, SVDD, FSVM-CIL, and WCS-FSVM is selected from the set {0.001,0.01,0.1,1,10,100}. In WCS-FSVM, β is selected from log⁡2β∈{-5,-4.5,-4,…,5.5,6}. For FSVM-CIL, the fuzzy membership is based on the distance from the actual hyperplane and uses the exponential fuzzy membership λ. λ is chosen from the range λ={0.1,0.2,0.3,…,1}.For the kernel-based methods, we use a Gaussian RBF kernel, that is,exp⁡(-(u-v)T(u-v)/σ), where σ is the spread of Gaussian kernel, and σ is searched in {τ2/16,τ2/8,τ2/4,τ2/2,τ2,2τ2,4τ2,8τ2,16τ2}, where τ2 is the mean norm of the training data.For parameter selection, we conduct fivefold cross-validation in a stratified manner so that each validation set has the same positive to negative ratio as in the training set. Finally, the experiment is repeated 10 times independently of each dataset.


### 5.3. Experimental Results

FSVM-CIP method test results developed for the breast, heart, hepatitis, BUPA liver, and pima diabetes datasets are given both in the linear case and nonlinear case. Tables2, 3, 4, 5, and 6 display the comparison results with the other methods on these five databases, respectively.Table 2
Comparison of the classification results (%) on breast dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
95.87
±
0.017
95.04
±
0.043
95.58
±
0.035
SVDD
97.71
±
0.065
90.90
±
0.013
95.28
±
0.052
FSVM-CIL
95.87
±
0.024
95.87
±
0.015
95.81
±
0.028
WCS-FSVM
96.33
±
0.067
95.04
±
0.056
95.87
±
0.047
FSVM-CIPlin
96.98
±
0.039
96.49
±
0.022
96.76
±
0.040
FSVM-CIPexp
96.68
±
0.011
96.69
±
0.042
96.76
±
0.037
Gaussian kernel
FSVM
96.33
±
0.023
95.87
±
0.051
96.17
±
0.050
SVDD
97.30
±
0.065
91.25
±
0.013
95.44
±
0.052
FSVM-CIL
96.79
±
0.059
95.87
±
0.042
96.46
±
0.055
WCS-FSVM
96.97
±
0.030
96.69
±
0.093
96.76
±
0.067
FSVM-CIPlin
97.25
±
0.055
96.29
±
0.032
97.05
±
0.042
FSVM-CIPexp
97.25
±
0.055
97.52
±
0.045
97.34
±
0.033Table 3
Comparison of the classification results (%) on heart dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
87.50
±
0.080
80.77
±
0.069
82.35
±
0.069
SVDD
87.03
±
0.021
77.69
±
0.005
80.00
±
0.051
FSVM-CIL
85.00
±
0.046
82.04
±
0.110
82.35
±
0.072
WCS-FSVM
87.30
±
0.071
81.54
±
0.089
82.94
±
0.088
FSVM-CIPlin
85.00
±
0.063
82.31
±
0.083
82.84
±
0.054
FSVM-CIPexp
87.50
±
0.025
82.31
±
0.083
83.53
±
0.055
Gaussian kernel
FSVM
86.70
±
0.099
82.61
±
0.087
83.35
±
0.042
SVDD
90.35
±
0.022
80.77
±
0.034
82.80
±
0.070
FSVM-CIL
87.05
±
0.034
81.54
±
0.067
82.94
±
0.044
WCS-FSVM
91.00
±
0.076
81.73
±
0.083
84.12
±
0.085
FSVM-CIPlin
90.00
±
0.045
82.31
±
0.086
84.12
±
0.052
FSVM-CIPexp
86.05
±
0.023
83.08
±
0.078
84.71
±
0.066Table 4
Comparison of the classification results (%) on hepatitis dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
82.60
±
0.053
22.73
±
0.087
53.33
±
0.073
SVDD
73.91
±
0.071
45.45
±
0.011
60.00
±
0.046
FSVM-CIL
77.66
±
0.026
45.46
±
0.082
61.02
±
0.070
WCS-FSVM
79.56
±
0.107
27.27
±
0.062
53.33
±
0.059
FSVM-CIPlin
78.26
±
0.046
45.46
±
0.032
62.22
±
0.023
FSVM-CIPexp
78.26
±
0.068
50.00
±
0.086
64.44
±
0.071
Gaussian kernel
FSVM
73.91
±
0.038
31.82
±
0.012
53.33
±
0.025
SVDD
82.60
±
0.053
42.86
±
0.025
63.64
±
0.030
FSVM-CIL
77.26
±
0.041
50.00
±
0.086
63.84
±
0.064
WCS-FSVM
78.26
±
0.015
36.36
±
0.074
57.78
±
0.056
FSVM-CIPlin
73.51
±
0.064
54.55
±
0.037
64.44
±
0.058
FSVM-CIPexp
73.91
±
0.050
59.10
±
0.011
66.67
±
0.036Table 5
Comparison of the classification results (%) on BUPA liver dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
88.10
±
0.008
66.42
±
0.073
72.19
±
0.057
SVDD
87.27
±
0.021
68.05
±
0.063
72.72
±
0.042
FSVM-CIL
88.00
±
0.004
67.44
±
0.042
73.19
±
0.015
WCS-FSVM
84.00
±
0.360
67.15
±
0.068
71.66
±
0.051
FSVM-CIPlin
88.00
±
0.004
67.88
±
0.063
73.26
±
0.031
FSVM-CIPexp
86.00
±
0.048
69.34
±
0.072
73.80
±
0.054
Gaussian kernel
FSVM
96.00
±
0.057
66.67
±
0.026
74.60
±
0.038
SVDD
95.43
±
0.033
71.24
±
0.050
77.23
±
0.017
FSVM-CIL
95.00
±
0.045
72.59
±
0.052
78.37
±
0.050
WCS-FSVM
90.08
±
0.070
67.44
±
0.083
73.73
±
0.062
FSVM-CIPlin
94.00
±
0.049
74.10
±
0.045
79.46
±
0.048
FSVM-CIPexp
94.00
±
0.049
73.33
±
0.084
79.92
±
0.074Table 6
Comparison of the classification results (%) on pima diabetes dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
91.91
±
0.022
49.98
±
0.053
55.36
±
0.051
SVDD
88.65
±
0.081
53.43
±
0.062
58.45
±
0.029
FSVM-CIL
86.36
±
0.064
55.10
±
0.059
59.86
±
0.060
WCS-FSVM
87.50
±
0.043
52.65
±
0.024
57.96
±
0.030
FSVM-CIPlin
85.23
±
0.021
57.76
±
0.064
61.94
±
0.043
FSVM-CIPexp
84.09
±
0.009
57.96
±
0.062
61.94
±
0.053
Gaussian kernel
FSVM
93.18
±
0.031
51.02
±
0.073
57.44
±
0.053
SVDD
91.76
±
0.025
56.86
±
0.052
62.57
±
0.028
FSVM-CIL
90.91
±
0.047
58.78
±
0.084
63.67
±
0.077
WCS-FSVM
92.05
±
0.010
54.69
±
0.066
60.38
±
0.053
FSVM-CIPlin
88.84
±
0.040
61.38
±
0.063
65.57
±
0.063
FSVM-CIPexp
88.64
±
0.029
61.43
±
0.074
65.57
±
0.070The main observations from the performance comparisons include the following.(1) We can see that, in many real-world applications, a linear classifier seems powerless. In terms of accuracy, kernel method can improve the classification performance for all five medical datasets.(2) We can clearly observe that the FSVM-CIP outperforms other methods on almost datasets both in the linear case and nonlinear case, which gives higher accuracy. This fortifies the fact that the locality maximum margin and the local structure information presented by local within-class preserving scatter could improve classification performance; furthermore, the method of different misclassification costs based on the number of two classes is a sensitive learning solution to overcome the imbalance problem in SVMs.(3) It is noted that, for all the datasets considered, the classification accuracy given by theFSVM-CIPexp⁡ setting is higher than the FSVM-CIPlin⁡ setting. Therefore, we can state that FSVM-CIPexp⁡ setting with the appropriate selection of λ value would be an effective choice applied to any medical dataset. In other words, when dealing with medical datasets classification, the performance of the exponential fuzzy membership is better than linear fuzzy membership in FSVM-CIP.(4) For breast and heart datasets, the class imbalance is not obviously shaped; WCS-FSVM yielded standard FSVM, SVDD, and FSVM-CIL. We can say that the performance can indeed be improved when the structure of the data is taken into consideration. For the other three datasets, the class imbalance strikingly improved, the results given by standard FSVM and WCS-FSVM for datasets are biased towards the majority class represented as lower specificity and lower accuracy. These results justify the fact that these two methods are sensitive to the class imbalance problem. Meanwhile, SVDD and FSVM-CIL yielded standard FSVM and WCS-FSVM. BY assigning different misclassification costs for the minority class and majority class, the effect of class imbalance could be reduced.


### 5.4. Parameter Selection for KernelFSVM-CIPexp⁡

The parameterη>0 is an essential parameter in our proposed method which controls the tradeoff between the local within-class scatter and the margin. Figure 2 shows the impact of parameter η on the classification accuracy of FSVM-CIPexp⁡ in kernel case with each value of η selected from log⁡2η∈{-5,-4.5,-4,…,5.5,6}. It can be seen that the best accuracy is obtained for all the datasets and therefore η is searched in a reasonable range.Figure 2
The effect of the parameterη on kernel FSVM-CIPexp⁡.Compared with standard FSVM, the additional neighbor parameterk is employed in FSVM-CIP. To evaluate the influence of this parameter on the performance, the classification accuracy of kernel FSVM-CIPexp⁡ for five medical databases is recorded for each value of k in {3,5,7,9,11,13,15}. Figure 3 shows the results. It can be seen that the classification accuracy is not high when k value is small and, by increasing k, the classification accuracy increases; however, if k continues to increase, the classification accuracy begins to drop severely down. It is because, when k is too small, the number of nearest neighbors is sparse; when k is too large, the number of nearest neighbors is excessive, so to preserve so much local relation may be inappropriate.Figure 3
The effect of the parameterk on kernel FSVM-CIPexp⁡.


## 5.1. Data Preparation

In this section, we use five real-world medical datasets from the UCI repository of machine learning database [24], to demonstrate the classification performance of the method proposed in this paper. These five medical datasets are breast, heart, hepatitis, BUPA liver, and pima diabetes. It is highly likely that these real-world datasets contain some outliers and noisy examples in different amounts [22]. In each of them, the positive class consists of the data corresponding to the healthy, normal, or benign cases, while the negative class contains the data for diseased, abnormal, or malignant cases. Further details of these datasets are provided in Table 1. This contains the total number of positive data #pos, the total number of negative data #neg, the number of positive training examples m1, the number of negative training examples m2, the positive-to-negative imbalance ratio Ratio, and the data dimensionality d.Table 1
Characteristics of the selected datasets.
Datasets
#pos
#neg
m
1
m
2
Ratio
d
Breast
458
241
240
120
2 : 1
9
Heart
120
150
80
20
4 : 1
13
Hepatitis
123
32
100
10
10 : 1
19
BUPA liver
200
145
150
10
15 : 1
6
Pima diabetes
268
500
180
10
18 : 1
8


## 5.2. Performance Measure and Experimental Settings

We used the geometric mean of sensitivity (sensitivity = proportion of the positives correctly recognized), specificity (specificity = proportion of the negatives correctly recognized), and accuracy (accuracy = proportion of correctly classified instances) for the classifier performance evaluation in experiments, as commonly used in medical datasets classification research [7].Like the existing SVM and FSVM algorithms, the solution is sensitive to the setting of the parameters. In order to evaluate the performance, a strategy is that a set of the parameters is given first and then the best cross-validation mean rate among the set is used to estimate the generalized accuracy. We adopt this strategy in this paper. For FSVM-CIP, the parameterν is searched in {1,5,10,15,…,80}, while v1 and v2 are selected from {0.001,0.005,0.01,0.05}. η is selected from log⁡2η∈{-5,-4.5,-4,…,5.5,6}. The heat kernel parameter t is searched in {0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0} and the neighborhood parameter k is searched in {3,5,7,9,11,13,15}. In addition, when the linear fuzzy function is used, we set δ=10-6. When the exponential fuzzy function is used, the optimal value of λ is chosen from the range λ={0.1,0.2,0.3,…,1}.The regularization parameterC for FSVM, SVDD, FSVM-CIL, and WCS-FSVM is selected from the set {0.001,0.01,0.1,1,10,100}. In WCS-FSVM, β is selected from log⁡2β∈{-5,-4.5,-4,…,5.5,6}. For FSVM-CIL, the fuzzy membership is based on the distance from the actual hyperplane and uses the exponential fuzzy membership λ. λ is chosen from the range λ={0.1,0.2,0.3,…,1}.For the kernel-based methods, we use a Gaussian RBF kernel, that is,exp⁡(-(u-v)T(u-v)/σ), where σ is the spread of Gaussian kernel, and σ is searched in {τ2/16,τ2/8,τ2/4,τ2/2,τ2,2τ2,4τ2,8τ2,16τ2}, where τ2 is the mean norm of the training data.For parameter selection, we conduct fivefold cross-validation in a stratified manner so that each validation set has the same positive to negative ratio as in the training set. Finally, the experiment is repeated 10 times independently of each dataset.


## 5.3. Experimental Results

FSVM-CIP method test results developed for the breast, heart, hepatitis, BUPA liver, and pima diabetes datasets are given both in the linear case and nonlinear case. Tables2, 3, 4, 5, and 6 display the comparison results with the other methods on these five databases, respectively.Table 2
Comparison of the classification results (%) on breast dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
95.87
±
0.017
95.04
±
0.043
95.58
±
0.035
SVDD
97.71
±
0.065
90.90
±
0.013
95.28
±
0.052
FSVM-CIL
95.87
±
0.024
95.87
±
0.015
95.81
±
0.028
WCS-FSVM
96.33
±
0.067
95.04
±
0.056
95.87
±
0.047
FSVM-CIPlin
96.98
±
0.039
96.49
±
0.022
96.76
±
0.040
FSVM-CIPexp
96.68
±
0.011
96.69
±
0.042
96.76
±
0.037
Gaussian kernel
FSVM
96.33
±
0.023
95.87
±
0.051
96.17
±
0.050
SVDD
97.30
±
0.065
91.25
±
0.013
95.44
±
0.052
FSVM-CIL
96.79
±
0.059
95.87
±
0.042
96.46
±
0.055
WCS-FSVM
96.97
±
0.030
96.69
±
0.093
96.76
±
0.067
FSVM-CIPlin
97.25
±
0.055
96.29
±
0.032
97.05
±
0.042
FSVM-CIPexp
97.25
±
0.055
97.52
±
0.045
97.34
±
0.033Table 3
Comparison of the classification results (%) on heart dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
87.50
±
0.080
80.77
±
0.069
82.35
±
0.069
SVDD
87.03
±
0.021
77.69
±
0.005
80.00
±
0.051
FSVM-CIL
85.00
±
0.046
82.04
±
0.110
82.35
±
0.072
WCS-FSVM
87.30
±
0.071
81.54
±
0.089
82.94
±
0.088
FSVM-CIPlin
85.00
±
0.063
82.31
±
0.083
82.84
±
0.054
FSVM-CIPexp
87.50
±
0.025
82.31
±
0.083
83.53
±
0.055
Gaussian kernel
FSVM
86.70
±
0.099
82.61
±
0.087
83.35
±
0.042
SVDD
90.35
±
0.022
80.77
±
0.034
82.80
±
0.070
FSVM-CIL
87.05
±
0.034
81.54
±
0.067
82.94
±
0.044
WCS-FSVM
91.00
±
0.076
81.73
±
0.083
84.12
±
0.085
FSVM-CIPlin
90.00
±
0.045
82.31
±
0.086
84.12
±
0.052
FSVM-CIPexp
86.05
±
0.023
83.08
±
0.078
84.71
±
0.066Table 4
Comparison of the classification results (%) on hepatitis dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
82.60
±
0.053
22.73
±
0.087
53.33
±
0.073
SVDD
73.91
±
0.071
45.45
±
0.011
60.00
±
0.046
FSVM-CIL
77.66
±
0.026
45.46
±
0.082
61.02
±
0.070
WCS-FSVM
79.56
±
0.107
27.27
±
0.062
53.33
±
0.059
FSVM-CIPlin
78.26
±
0.046
45.46
±
0.032
62.22
±
0.023
FSVM-CIPexp
78.26
±
0.068
50.00
±
0.086
64.44
±
0.071
Gaussian kernel
FSVM
73.91
±
0.038
31.82
±
0.012
53.33
±
0.025
SVDD
82.60
±
0.053
42.86
±
0.025
63.64
±
0.030
FSVM-CIL
77.26
±
0.041
50.00
±
0.086
63.84
±
0.064
WCS-FSVM
78.26
±
0.015
36.36
±
0.074
57.78
±
0.056
FSVM-CIPlin
73.51
±
0.064
54.55
±
0.037
64.44
±
0.058
FSVM-CIPexp
73.91
±
0.050
59.10
±
0.011
66.67
±
0.036Table 5
Comparison of the classification results (%) on BUPA liver dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
88.10
±
0.008
66.42
±
0.073
72.19
±
0.057
SVDD
87.27
±
0.021
68.05
±
0.063
72.72
±
0.042
FSVM-CIL
88.00
±
0.004
67.44
±
0.042
73.19
±
0.015
WCS-FSVM
84.00
±
0.360
67.15
±
0.068
71.66
±
0.051
FSVM-CIPlin
88.00
±
0.004
67.88
±
0.063
73.26
±
0.031
FSVM-CIPexp
86.00
±
0.048
69.34
±
0.072
73.80
±
0.054
Gaussian kernel
FSVM
96.00
±
0.057
66.67
±
0.026
74.60
±
0.038
SVDD
95.43
±
0.033
71.24
±
0.050
77.23
±
0.017
FSVM-CIL
95.00
±
0.045
72.59
±
0.052
78.37
±
0.050
WCS-FSVM
90.08
±
0.070
67.44
±
0.083
73.73
±
0.062
FSVM-CIPlin
94.00
±
0.049
74.10
±
0.045
79.46
±
0.048
FSVM-CIPexp
94.00
±
0.049
73.33
±
0.084
79.92
±
0.074Table 6
Comparison of the classification results (%) on pima diabetes dataset.
Method
Sensitivity
Specificity
Accuracy
Linear
FSVM
91.91
±
0.022
49.98
±
0.053
55.36
±
0.051
SVDD
88.65
±
0.081
53.43
±
0.062
58.45
±
0.029
FSVM-CIL
86.36
±
0.064
55.10
±
0.059
59.86
±
0.060
WCS-FSVM
87.50
±
0.043
52.65
±
0.024
57.96
±
0.030
FSVM-CIPlin
85.23
±
0.021
57.76
±
0.064
61.94
±
0.043
FSVM-CIPexp
84.09
±
0.009
57.96
±
0.062
61.94
±
0.053
Gaussian kernel
FSVM
93.18
±
0.031
51.02
±
0.073
57.44
±
0.053
SVDD
91.76
±
0.025
56.86
±
0.052
62.57
±
0.028
FSVM-CIL
90.91
±
0.047
58.78
±
0.084
63.67
±
0.077
WCS-FSVM
92.05
±
0.010
54.69
±
0.066
60.38
±
0.053
FSVM-CIPlin
88.84
±
0.040
61.38
±
0.063
65.57
±
0.063
FSVM-CIPexp
88.64
±
0.029
61.43
±
0.074
65.57
±
0.070The main observations from the performance comparisons include the following.(1) We can see that, in many real-world applications, a linear classifier seems powerless. In terms of accuracy, kernel method can improve the classification performance for all five medical datasets.(2) We can clearly observe that the FSVM-CIP outperforms other methods on almost datasets both in the linear case and nonlinear case, which gives higher accuracy. This fortifies the fact that the locality maximum margin and the local structure information presented by local within-class preserving scatter could improve classification performance; furthermore, the method of different misclassification costs based on the number of two classes is a sensitive learning solution to overcome the imbalance problem in SVMs.(3) It is noted that, for all the datasets considered, the classification accuracy given by theFSVM-CIPexp⁡ setting is higher than the FSVM-CIPlin⁡ setting. Therefore, we can state that FSVM-CIPexp⁡ setting with the appropriate selection of λ value would be an effective choice applied to any medical dataset. In other words, when dealing with medical datasets classification, the performance of the exponential fuzzy membership is better than linear fuzzy membership in FSVM-CIP.(4) For breast and heart datasets, the class imbalance is not obviously shaped; WCS-FSVM yielded standard FSVM, SVDD, and FSVM-CIL. We can say that the performance can indeed be improved when the structure of the data is taken into consideration. For the other three datasets, the class imbalance strikingly improved, the results given by standard FSVM and WCS-FSVM for datasets are biased towards the majority class represented as lower specificity and lower accuracy. These results justify the fact that these two methods are sensitive to the class imbalance problem. Meanwhile, SVDD and FSVM-CIL yielded standard FSVM and WCS-FSVM. BY assigning different misclassification costs for the minority class and majority class, the effect of class imbalance could be reduced.


## 5.4. Parameter Selection for KernelFSVM-CIPexp⁡

The parameterη>0 is an essential parameter in our proposed method which controls the tradeoff between the local within-class scatter and the margin. Figure 2 shows the impact of parameter η on the classification accuracy of FSVM-CIPexp⁡ in kernel case with each value of η selected from log⁡2η∈{-5,-4.5,-4,…,5.5,6}. It can be seen that the best accuracy is obtained for all the datasets and therefore η is searched in a reasonable range.Figure 2
The effect of the parameterη on kernel FSVM-CIPexp⁡.Compared with standard FSVM, the additional neighbor parameterk is employed in FSVM-CIP. To evaluate the influence of this parameter on the performance, the classification accuracy of kernel FSVM-CIPexp⁡ for five medical databases is recorded for each value of k in {3,5,7,9,11,13,15}. Figure 3 shows the results. It can be seen that the classification accuracy is not high when k value is small and, by increasing k, the classification accuracy increases; however, if k continues to increase, the classification accuracy begins to drop severely down. It is because, when k is too small, the number of nearest neighbors is sparse; when k is too large, the number of nearest neighbors is excessive, so to preserve so much local relation may be inappropriate.Figure 3
The effect of the parameterk on kernel FSVM-CIPexp⁡.


## 6. Conclusion

Computer tools have improved the medical practice implementation to a greater extent. Although computer tools cannot replace the doctors, they can make their work easier and more effective. In this paper, a new fuzzy support machine called FSVM-CIP, used for medical datasets classification, is proposed. The proposed method is based on local within-class preserving scatter and assigned two misclassification costs in the SVM objective function, which is for learning from imbalance datasets in the presence of outliers/noise and enhancing the locality maximum margin. Experiments were performed on several UCI medical datasets with a comparison of the proposed method with several other related methods such as standard FSVM, SVDD, FSVM-CIL, and WCS-FSVM. Obtained results show that the performance of the proposed method is highly successful compared to other results attained and seems very promising. Finally, we can recommend thatFSVM-CIPexp⁡ which uses the exponential fuzzy membership would be an effective choice for medical datasets classification applications. In future work, we intend to perform investigations to large-scale classification problems.



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*Source: 536434-2014-03-23.xml*