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

2020 ◽  
Vol 10 (3) ◽  
pp. 1065 ◽  
Author(s):  
Wei Liu ◽  
LinLin Ci ◽  
LiPing Liu
Keyword(s):  
Support Vector Machine ◽  
Membership Function ◽  
Sequence Data ◽  
Weight Coefficient ◽  
Cluster Center ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine

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.

A New Fuzzy Support Vector Machine for Credit Scoring

2011 ◽  
Vol 109 ◽  
pp. 636-640
Author(s):  
Bo Tang ◽  
Min Xia
Keyword(s):  
Support Vector Machine ◽  
Economic Development ◽  
Prediction Accuracy ◽  
Credit Scoring ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Good Prediction ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine ◽  
Good Prediction Accuracy

With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.

scholarly journals K-MEANS SEBAGAI EKSTRAKTOR CIRI PADA KLASIFIKASI DATA DENGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

2018 ◽  
Vol 9 (2) ◽  
pp. 889-896
Author(s):  
Nurul Chamidah
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Membership Function ◽  
Cross Validation ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Support Vector ◽  
Fold Cross Validation

Besarnya dimensi pada ciri merupakan masalah pada komputasi untuk mengklasifikasi data sehingga diperlukan suatu proses ekstraksi ciri agar dimensinya berkurang dengan cara mengambil hanya informasi yang penting dari ciri. Penelitian ini menggunakan algoritma K-Means untuk mengekstraksi ciri dengan menemukan pola tersembunyi dari setiap kelas kemudian direkonstruksi dengan fuzzy membership function dan mendapatkan pola baru. Pola baru yang terbentuk digunakan sebagai  ciri abstrak dan dibagi kedalam data latih dan data uji. Pelatihan dilakukan dengan memanfaatkan algoritma Support Vector Machine (SVM) untuk mendapatkan model klasifikasi. Model klasifikasi SVM yang diperoleh kemudian di uji dengan menggunakan data uji untuk memperoleh performa klasifikasi berupa akurasi dan waktu komputasi. Dengan 5-fold cross validation, metode ini memberikan akurasi yang baik pada dataset Liver, Breast Cancer dan Heart Disease yang diperoleh dari UCI Machine Learning Repository. Penelitian ini menunjukkan kemampuan K-Means untuk mengekstraksi ciri dari dataset. Hasil penelitian ini menujukkan bahwa K-Means sebagai ekstraktor ciri dapat mengurangi waktu komputasi.

Membership Calculation Based on Dimension Hierarchical Division

2013 ◽  
Vol 475-476 ◽  
pp. 312-317
Author(s):  
Ping Zhou ◽  
Jin Lei Wang ◽  
Xian Kai Chen ◽  
Guan Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Bottom Up ◽  
Generalization Ability ◽  
Clustering Technique ◽  
Novel Approach ◽  
Training Samples ◽  
Noise Sample

Since dataset usually contain noises, it is very helpful to find out and remove the noise in a preprocessing step. Fuzzy membership can measure a samples weight. The weight should be smaller for noise sample but bigger for important sample. Therefore, appropriate sample memberships are vital. The article proposed a novel approach, Membership Calculate based on Hierarchical Division (MCHD), to calculate the membership of training samples. MCHD uses the conception of dimension similarity, which develop a bottom-up clustering technique to calculate the sample membership iteratively. The experiment indicates that MCHD can effectively detect noise and removes them from the dataset. Fuzzy support vector machine based on MCHD outperforms most of approaches published recently and hold the better generalization ability to handle the noise.

scholarly journals The use of entropy based fuzzy membership on weighted logistic regression for the unbalanced data

2021 ◽  
Vol 880 (1) ◽  
pp. 012048
Author(s):  
Ajiwasesa Harumeka ◽  
Santi Wulan Purnami ◽  
Santi Puteri Rahayu
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Rare Event ◽  
Least Square ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Unbalanced Data ◽  
Fuzzy Support Vector Machine ◽  
Minority Class ◽  
Weighted Logistic Regression

Abstract Logistic regression is a popular and powerful classification method. The addition of ridge regularization and optimization using a combination of linear conjugate gradients and IRLS, called Truncated Regularized Iteratively Re-weighted Least Square (TR-IRLS), can outperform Support Vector Machine (SVM) in terms of processing speed, especially when applied to large data and have competitive accuracy. However, neither SVM nor TR-IRLS is good enough when applied to unbalanced data. Fuzzy Support Vector Machine (FSVM) is an SVM development for unbalanced data that adds fuzzy membership to each observation. The fuzzy membership makes the interest of each observation in the minority class higher than the majority class. Meanwhile, TR-IRLS developed into a Rare Event Weighted Logistic Regression (RE-WLR) by adding weight to logistic regression and bias correction. The weighting of the RE-WLR depends on the undersampling scheme. It allows an “information loss”. Between FSVM and RE-WLR has a similarity, the weight based only on class differences (minority or majority). Entropy Based Fuzzy Support Vector Machine (EFSVM) is a method used to accommodate the weaknesses of FSVM by considering the class certainty of class observations. As a result, EFSVM is able to improve SVM performance for unbalanced data, even beating FSVM. For this reason, we use EF on the TR-IRLS algorithm to classify large and unbalanced data, as a proposed method. This method is called Entropy-Based Fuzzy Weighted Logistic Regression (EF-WLR). This Research shows the review of EF-WLR for unbalanced data classification.

scholarly journals A New Multi-class Fuzzy Support Vector Machine Algorithm

2014 ◽  
pp. 153-164 ◽  
Author(s):  
Friedhelm Schwenker ◽  
Markus Frey ◽  
Michael Glodek ◽  
Markus Kächele ◽  
Sascha Meudt ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Fuzzy Support Vector Machine
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