A new fuzzy twin support vector machine for pattern classification

Chen, Su-Gen; Wu, Xiaojun

doi:10.1007/s13042-017-0664-x

Cited by 61 publications

(21 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVM is sensitive to noises and outliers [57][58][59][60]. FSVM is based on fuzzy theory to reduce the influence of noises or outliers on the classification hyperplane [61][62][63][64][65][66]. Lin et al [67] proposed a method based on the relation between samples and their cluster center.…”

Section: Previous Workmentioning

confidence: 99%

A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

Liu

2020

Applied Sciences

View full text Add to dashboard Cite

Since SVM is sensitive to noises and outliers of system call sequence data. A new fuzzy support vector machine algorithm based on SVDD is presented in this paper. In our algorithm, the noises and outliers are identified by a hypersphere with minimum volume while containing the maximum of the samples. The definition of fuzzy membership is considered by not only the relation between a sample and hyperplane, but also relation between samples. For each sample inside the hypersphere, the fuzzy membership function is a linear function of the distance between the sample and the hyperplane. The greater the distance, the greater the weight coefficient. For each sample outside the hypersphere, the membership function is an exponential function of the distance between the sample and the hyperplane. The greater the distance, the smaller the weight coefficient. Compared with the traditional fuzzy membership definition based on the relation between a sample and its cluster center, our method effectively distinguishes the noises or outlies from support vectors and assigns them appropriate weight coefficients even though they are distributed on the boundary between the positive and the negative classes. The experiments show that the fuzzy support vector proposed in this paper is more robust than the support vector machine and fuzzy support vector machines based on the distance of a sample and its cluster center.

show abstract

Section: Previous Workmentioning

confidence: 99%

A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

Liu

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…By solving the model (18), and obtaining optimal α and putting it in Eq. (16) or Eq. (17), the value of V, i.e.…”

Section: Linear Twsvc+mentioning

confidence: 99%

“…SVC is based on a well-known classification model named support vector machine (SVM) [13]. Training time and accuracy of Twin SVM (TWSVM) [14][15][16] are better than those of SVM. The existing TWSVM-based clustering, i.e.…”

Section: Introductionmentioning

confidence: 99%

TWSVC+: Improved Twin Support Vector Machine-Based Clustering

Moezzi¹,

Jalali²,

Forghani³

2019

ISI

View full text Add to dashboard Cite

Based on twin support vector machines (TWSVM) model, the twin support vector clustering (TWSVC) is a planar clustering model that increases inter-cluster separation. Because the TWSVC is not a standard model for some variables, its solving algorithm consumes lots of time and does not always converge to the optimal solution. To solve the problem, this paper proposes a novel clustering model, denoted as TWSVC+, based on twin support vector machines (TWSVM). The TWSVC+ is convex and standard with respect to each variable. Therefore, it is possible to solve this model rapidly with an algorithm that converges to a global optimal solution relative to each variable. The author presented linear TWSVC+ and non-linear TWSVC+ for clustering linear separable clusters and linear inseparable clusters, respectively. Experimental results on real datasets of UCI repository show that the TWSVC+ was better than TWSVC and support vector clustering (SVC) in accuracy and training time.

show abstract

“…In order to overcome this problem in FSVM, a new fuzzy SVM for imbalance datasets was proposed to reduce the misclassification accuracy of the minority class in FSVM [46]. In addition, based on utilizing fuzzy membership, various Fuzzy SVMs were presented such as NFSVM [47], Bilateral-weighted FSVM (BFSVM) [48], WCS-FSVM [49], FTSVM [50], NFTSVM [51] and FLTPMSVM [52], etc.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

et al. 2019

View full text Add to dashboard Cite

Twin support vector machine (TWSVM) is a new machine learning method, as opposed to solving a single quadratic programming problem in support vector machine (SVM), which generates two nonparallel hyperplanes by solving two smaller size quadratic programming problems. However, the TWSVM obtains the final classifier by giving the same importance to all training samples which may be important for classification performance. In order to address this problem, in this paper, we propose a novel entropy-based fuzzy twin bounded support vector machine (EFTBSVM) for binary classification problems. By considering the fuzzy membership value for each sample and assigning it based on the entropy value, the samples with higher class certainty are assigned to relatively larger fuzzy membership. In addition, the proposed EFTBSVM not only maintains the superior characteristics of the TWSVM but also exploits the structural risk minimization principle by introducing a regularization term. The experimental results achieved on synthetic datasets and benchmark datasets illustrate the effectiveness of the proposed method.

show abstract

A new fuzzy twin support vector machine for pattern classification

Cited by 61 publications

References 33 publications

A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

TWSVC+: Improved Twin Support Vector Machine-Based Clustering

Entropy-Based Fuzzy Twin Bounded Support Vector Machine for Binary Classification

Contact Info

Product

Resources

About