Gustafson-Kessel Clustering Algorithm Research Articles

Non-destructive Identification of the geographical origin of red jujube by near-infrared spectroscopy and fuzzy clustering methods

ABSTRACT The red jujube quality is closely associated with its place of origin. In order to quickly and easily identify the geographical origin of red jujube, the classification of red jujube samples’ near-infrared reflectance (NIR) spectra was performed using several fuzzy clustering methods in combination with principal component analysis (PCA) and linear discriminant analysis (LDA). Firstly, a NIR-M-R2 portable near-infrared spectrometer was used to collect four varieties of red jujube samples from four representative producing areas in four provinces: Gansu, Henan, Shanxi and Xinjiang in China. Each variety corresponded to a producing area, and it had 60 samples with a total of 240 samples. Near-infrared spectra of red jujube were acquired using a NIR-M-R2 portable near-infrared spectrometer, and the initial near-infrared spectra were preprocessed by Savitzky-Golay (SG) filtering. Secondly, PCA and LDA were used to further process the NIR data for dimension reduction and feature extraction, respectively. Finally, red jujube samples were classified by fuzzy C-means (FCM) clustering, Gustafson-Kessel (GK) clustering and possibility fuzzy C-means (PFCM) clustering. When GK served as the clustering algorithm, the clustering accuracy was the highest, as the value of 98.8%. Based on the experimental results, it was evident that the GK clustering algorithm played a significant role in identifying the place of origin of red jujube with near-infrared spectroscopy.

Double-suppressed possibilistic fuzzy Gustafson–Kessel clustering algorithm

Possibilistic fuzzy c-means clustering (PFCM) is an unsupervised hybrid clustering algorithm, which can partly inherit the stability of fuzzy c-means clustering (FCM) algorithm and the noise robustness of possibilistic c-means clustering (PCM) algorithm. However, there are still several limitations in the PFCM when clustering complex data with multiple characteristics: sensitivity to strong noise, coincident clustering problem in multi-class clustering, and disability to deal with strong correlations of feature components. Therefore, a double-suppressed possibilistic fuzzy Gustafson–Kessel​ clustering algorithm (DS-PFGK) is presented. Firstly, we design a new objective function with a modified Mahalanobis distance and an improved weight parameter setting method based on the stability of fuzzy memberships and the anti-noise ability of possibilistic memberships. Moreover, combined with the absolute attribute of possibilistic memberships and the covariance matrix in the modified Mahalanobis distance, an ellipsoidal cluster core which can depict the distribution of sample data is generated to divide each cluster into different regions for selecting appropriate learning objects and identifying noisy data adaptively. Then, a “double-suppressed competitive learning” strategy is designed for the selected learning objects to solve the coincident clustering problem in multi-class clustering by suppressing the nonwinner possibilistic memberships, and to reduce the iteration number by suppressing nonwinner fuzzy memberships and rewarding winner fuzzy memberships. Finally, a segmentation algorithm on noisy color images based on the DS-PFGK is proposed based on the generated cluster cores. Experiments on synthetic datasets, real datasets, and color images show the good performance of the proposed methods compared with several classical and state-of-the-art clustering algorithms.

Quantification of the ilio-femoral arteries tortuosity and data cluster modelling for preoperative examination

Detecting spatial tortuosity and atherosclerotic changes of the ilio-femoral arteries are crucial for planning endovascular access. The aim of this study was to find a reliable quantification procedure of arterial lumen and tortuosity to qualify patients for a suitable endovascular procedure. We conducted computed tomographic angiography in 76 patients. All ilio-femoral segments of the arterial tree were visualized using Osirix Dicom Viewer software to help qualify the patients to one of two groups: with possible or non-recommended vascular access. The same tomograms were then analyzed with image processing algorithms to perform ilio-femoral artery segmentation and quantification. We chose a set of arterial tortuosity and lumen measuring methods, such as the modified Gustafson-Kessel clustering algorithm and Support Vector Machine classifier, to automatically classify arterial-tree regions. The two 2D feature spaces were selected with the modified Gustafson-Kessel clusterization to create a combined model to assign around 2/3 cases to the access groups with high specificity (more than 88%) whereas the remaining patients were selected for re-evaluation. We concluded that the novel modification of the Gustafson-Kessel clustering algorithm is more suitable to the highly inseparable data than commonly used approaches. To identify ilio-femoral access limitations, we recommend more complex decision model. This study confirmed high usability of our chosen methodology in the quantitative examination of arteries for endovascular access planning.

Soft-sensing of effluent total phosphorus using adaptive recurrent fuzzy neural network with Gustafson-Kessel clustering

To address the issue of soft-sensing of effluent total phosphorus in wastewater treatment processes (WWTPs), a soft-sensing system based on an adaptive recursive fuzzy neural network with Gustafson-Kessel (GK) clustering and hierarchical adaptive second-order optimization algorithm (HAS) is proposed in this paper. In GK-ARFNN, first, the GK clustering algorithm was utilized to cluster the input–output dataset. Thus, the establishment of the initial fuzzy rule base and the determination of the parameter value of the fuzzy set membership function was realized. Then, the recursive layer was added into FNN to improve the dynamic mapping ability of the system. Finally, the HAS algorithm was developed based on the improved Levenberg-Marquardt (LM) optimization algorithm, and all the free parameters of the GK-ARFNN were adjusted online using HAS to improve the generalization capability and prediction accuracy of the soft-sensing system. In addition, the convergence of the proposed GK-ARFNN algorithm was also analyzed in this paper, which can ensure the effectiveness of the solutions to modelling issues for practical industrial processes. The simulation results demonstrate that the GK-ARFNN-based soft-sensing system introduced in this paper achieved satisfactory accuracy in the prediction of effluent total phosphorus in WWTPs. The source codes of GK-ARFNN and some competitors can be downloaded from https://github.com/hyitzhb/GK-ARFNN.

A fuzzy regression functions approach based on Gustafson-Kessel clustering algorithm

Fuzzy inference systems, referring to a system that works on fuzzy sets, have been used in many areas such as classification, information order, especially in the field of forecasting. Because fuzzy inference systems are based on certain rules, determining these rules is the most important problem of many well-known fuzzy inference systems in the literature. To overcome this problem, the Type-1 fuzzy regression functions approach uses fuzzy functions instead of relations, unlike many fuzzy inference systems that operate on a rule base and establish a compound relation between the input and output of the system. One of the most important success criteria of Type-1 fuzzy regression functions is the type of clustering method. The Gustafson Kessel clustering algorithm has no limitations unlike the fuzzy clustering algorithm and can recognize ellipsoidal clusters. In this study, the Gustafson Kessel clustering algorithm is used instead of the fuzzy clustering algorithm and thus the membership values of the input set are obtained with the Gustafson Kessel clustering algorithm in the structure of the fuzzy regression functions approach. The analysis results show that the forecasting performance is increased considerably with the use of the Gustafson Kessel clustering algorithm.

A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data.

In this paper, a methodology for design of fuzzy Kalman filter, using interval type-2 fuzzy models, in discrete time domain, via spectral decomposition of experimental data, is proposed. The adopted methodology consists of recursive parametric estimation of local state space linear submodels of interval type-2 fuzzy Kalman filter for tracking and forecasting of the dynamics inherited to experimental data, using an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm. The partitioning of the experimental data is performed by interval type-2 fuzzy Gustafson–Kessel clustering algorithm. The interval Kalman gains in the consequent proposition of interval type-2 fuzzy Kalman filter are updated according to unobservable components computed by recursive spectral decomposition of experimental data. Computational results illustrate the efficiency of proposed methodology for filtering and tracking the time delayed state variables of Chen’s chaotic attractor in a noisy environment, and experimental results illustrate its applicability for adaptive and real time forecasting the dynamic spread behavior of novel Coronavirus 2019 (COVID-19) outbreak in Brazil.

Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data

In this paper, a methodology for design of Kalman filter, using interval type-2 fuzzy systems, in discrete time domain, via spectral decomposition of experimental data, is proposed. The adopted methodology consists of recursive parametric estimation of local state space linear submodels of interval type-2 fuzzy Kalman filter for tracking and forecasting of the dynamics inherited to experimental data, using an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm. The partitioning of the experimental data is performed by interval type-2 fuzzy Gustafson–Kessel clustering algorithm. The interval Kalman gains in the consequent proposition of interval type-2 fuzzy Kalman filter are updated according to unobservable components computed by recursive spectral decomposition of experimental data. Results illustrate the efficiency of proposed methodology, as compared to other approach widely cited in the literature, for filtering and tracking the state variables of Lorenz’s chaotic attractor in a noisy environment, and its applicability for adaptive and real-time forecasting the dynamic spread behavior of novel coronavirus 2019 (COVID-19) outbreak in state of Maranhão and Brazil.

Automatic identification of the area covered by acorn trees in the dehesa (pastureland) Extremadura of Spain

The acorn is the fruit of oak trees and is an important crop in the Spanish dehesa extremeña because of the value it provides to feeding Iberian pigs to obtain the “acorn-fed” certification. For this reason, it is desirable to maximise production of Iberian pigs with the appropriate weight. Knowing the area covered by the acorn treetops to determine the Tree-Covered Area (TCA), a value required to estimate the number of Iberian pigs that can be released per hectare, as regulated by the Royal Decree 4/2014, issued by Spain in January of 2014. In this work, we propose a method to automatically estimate the TCA through digital aerial images (orthophotos) of the pastureland of Extremadura and, with this, to facilitate determining the number of Iberian pigs that can be released in a specific parcel of land. Requirements for automatic detection include the correct identification of acorn trees, correctly discerning their shadows, and discerning arbuscles (young acorn trees that are still unproductive, or shrubs that are not oaks). These requirements represent challenges that apply to both automatic and manual segmentations. The automated segmentation in this study is based on the modified version of the Gustafson-Kessel clustering algorithm presented by Babuska. The results of this work are promising when compared with the actual images and when compared with the results of manual segmentation. The entire set of 38 aerial images used in this work correspond to an approximate area of 15.56 hectares, and the results are of great interest to producers of Iberian pork with the “acorn-fed” certification.

Evaluation of the Physical Properties of Bedding Materials for Dairy Cattle Using Fuzzy Clustering Analysis.

Simple SummaryThe bedding material used in dairy cow housing systems plays a key role in animal welfare and performance, since it influences the time that the animals remain lying down. The primary aim of this paper was to evaluate the physical properties of different bedding materials for dairy cattle and, further, to employ different fuzzy clustering algorithms to effectively cluster these alternative materials based on their physical properties. To perform nine physical analyses, 51 different bedding materials from various places in Europe were used. These data were analysed by principal components analysis (PCA) and then by fuzzy clustering analysis. Three clustering algorithms were tested for different numbers of clusters (2–8). They were compared by five validation indexes to choose the best clustering algorithm and the number of clusters. By these analyses it was possible to conclude that alternative materials can be classified based on their physical properties. The Gustafson–Kessel (GK) clustering algorithms, with eight clusters, fit better regarding the division of materials according to their properties. Posidonia oceanica showed potential to be used as an alternative bedding material due to its favourable physical properties.The bedding materials used in dairy cow housing systems are extremely important for animal welfare and performance. A wide range of materials can be used as bedding for dairy cattle, but their physical properties must be analysed to evaluate their potential. In the present study, the physical properties of various bedding materials for dairy cattle were investigated, and different fuzzy clustering algorithms were employed to cluster these materials based on their physical properties. A total of 51 different bedding materials from various places in Europe were collected and tested. Physical analyses were carried out for the following parameters: bulk density (BD), water holding capacity (WHC), air-filled porosity (AFP), global density (GD), container capacity (CC), total effective porosity (TEP), saturated humidity (SH), humidity (H), and average particle size (APS). These data were analysed by principal components analysis (PCA) to reduce the amount of data and, subsequently, by fuzzy clustering analysis. Three clustering algorithms were tested: k-means (KM), fuzzy c-means (FCM) and Gustafson–Kessel (GK) algorithms. Furthermore, different numbers of clusters (2−8) were evaluated and subsequently compared using five validation indexes. The GK clustering algorithm with eight clusters fit better regarding the division of materials according to their properties. From this clustering analysis, it was possible to understand how the physical properties of the bedding materials may influence their behaviour. Among the materials that fit better as bedding materials for dairy cows, Posidonia oceanica (Cluster 6) can be considered an alternative material.

IMPLEMENTASI ALGORITMA MODIFIED GUSTAFSON-KESSEL UNTUK CLUSTERING TWEETS PADA AKUN TWITTER LAZADA INDONESIA

Online social media is a new kind of media which is steadily growing and has become publicly popular. Due to its ability to spread informations rapidly and its easiness to access for internet users, social media provides new alternative to conduct advertising and product segmentation. Twitter is one of the most favored social media with 19.5 million users in Indonesia to the date. In this research, the application of text mining to cluster tweets from the @LazadaID Twitter account is done using the Modified Gustafson-Kessel clustering algorithm. The clustering process is executed five times with the number of cluster starts from two to six cluster. The results of this research indicate that the optimum number of clusters formed based on the Partition Coefficient and Classification Entropy validation index are three clusters. Those three clusters are tweets containing electronic stuff offers, discounts, and prize quizes. Tweets with the most retweets and likes are prize quiz tweets. PT Lazada Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @LazadaID Twitter account followers.Keywords: Twitter, advertising, Lazada Indonesia, Gustafson-Kessel Clustering algorithm, validation index

Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling

This paper proposes a fuzzy weighted least squares support vector regression (FW-LSSVR) with data reduction for nonlinear system modeling based only on the measured data. The proposed method combines the advantages of data reduction with some ideas of fuzzy weighted mechanism. It not only possesses the capability of illuminating local characteristic of the modeled plant but also can deal with the problem of boundary effects resulted from local LSSVR method when the modeled data is at the boundary of whole data subset. Furthermore, in comparison of the SVR, the proposed method only utilizes fewer hyperparameters to construct model, and the overlap factor λ can be chosen in relatively smaller value than SVR to further reduce more computational time. First of all, distilling the original input space into several regions with fuzzy partition by applying Gustafson-Kessel clustering algorithm (GKCA) is a foundation for data reduction and the overlap factor is introduced to reduce the size of subsets. Following that, those subset regression models (SRMs) which can be simultaneously solved by LSSVR are integrated into an overall output of the estimated nonlinear system by fuzzy weighted. Finally, the proposed method is demonstrated by experimental analysis and compared with local LSSVR, weighted SVR, and global LSSVR methods by using the index of computational time and root-mean-square error (RMSE).

Integration of weighted LS-SVM and manifold learning for fuzzy modeling

Abstract In this paper, a robust fuzzy modeling method is proposed for strongly nonlinear systems in the presence of noise and/or outliers. The proposed method integrates the advantages of the fuzzy structure, the manifold learning, and the weighted least squares support vector machine (LS-SVM). First, the Gustafson–Kessel clustering algorithm (GKCA) is applied to split the training data set into several subsets to determine the fuzzy rules and premise parameters. Then, a new objective function is constructed based on the fuzzy structure, the weighted LS-SVM, and the manifold regularization, which takes into account robustness and the intrinsic geometry of the data. A solving method is further developed, from which the fuzzy model is achieved and can effectively approximate a nonlinear system with various types of random noise. The proposed method is applied to an artificial case as well as a practical hydraulic actuator, demonstrating its effectiveness in modeling of a nonlinear system even under noise.

Clustering Accuracies on Concepts of Mathematics

Fuzzy c-means algorithm (FCM) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, Gustafson-Kessel clustering algorithm needs added constraint of fuzzy covariance matrix, Gath-Geva clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. Improved Normalized Mahalanobis Clustering Algorithm Based on FCM by taking a new threshold value and a new convergent process is proposed. The experimental results of real data sets show that our proposed new algorithm has the best performance. Not only replacing the common covariance matrix with the correlation matrix in the objective function in the Normalized Mahalanobis Clustering Algorithm.

Temperature Uniformity Control of Large-Scale Vertical Quench Furnaces for Aluminum Alloy Thermal Treatment

The thermal treatment of aluminum alloy workpieces requires strict temperature uniformity in large-scale vertical quench furnaces. To achieve the desired temperature uniformity in a large-scale spatial setting, a temperature uniformity control strategy combining workpiece temperature compensation and intelligent proportional–integral–derivative (PID) decoupling control is presented. The temperature compensation of the workpiece is realized by establishing an air heat conduction model. Moreover, an intelligent PID decoupling control system based on a novel self-growing radial basis function neural network (SGRBFNN) is developed to eliminate the strong coupling effects of multiheating zones. SGRBFNN, with the structure being dynamically adjusted by a hybrid semifuzzy Gustafson–Kessel clustering algorithm, is proposed to realize the online tuning of the parameters of the PID controller. Both the simulation and industrial experiment results demonstrate that the proposed temperature control system can effectively achieve smooth regulations and significantly improve temperature uniformity. The application also demonstrates the validity and better control performance of the proposed system compared with conventional control systems.

Multi-stage design space reduction and metamodeling optimization method based on self-organizing maps and fuzzy clustering

Design space exploration and metamodeling techniques have gained rapid dominance in complex engineering design problems. It is observed that the modeling efficiency and accuracy are directly associated with the design space. In order to reduce the complexity of the design space and improve modeling accuracy, a multi-stage design space reduction and metamodeling optimization methodology based on self-organizing maps and fuzzy clustering is proposed in this paper. By using the proposed three-stage optimization approach, the design space is systematically reduced to a relatively small promising region. Self-organizing maps are introduced to act as the preliminary reduction approach through analyzing the underlying mapping relations between design variables and system responses within the original samples. GK (Gustafson & Kessel) clustering algorithm is employed to determine the proper number of clusters by utilizing clustering validity indices, and sample points are clustered using fuzzy c-means (FCM) clustering method with the known number of clusters, so that the search can focus on the most promising area and be better supported by the constructed kriging metamodel. Empirical studies on benchmark problems with multi-humps and two practical nonlinear engineering design problems show the accurate results can be obtained within the reduced design space, which improve the overall efficiency significantly.

A Novel Evolutionary Kernel Intuitionistic Fuzzy <formula formulatype="inline"><tex Notation="TeX">$C$</tex></formula> -means Clustering Algorithm

This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel-based fuzzy c-means (KFCM), and genetic algorithms (GA) are optimally used simultaneously to select the parameters of the EKIFCM. The EKIFCM can obtain the advantages of intuitionistic fuzzy sets, kernel functions, and GA in actual clustering problems. Experiments on 2-D synthetic datasets and machine learning repository (http://archive.ics.uci.edu/beta/) datasets show that the proposed EKIFCM is more efficient than conventional algorithms such as the k-means (KM), FCM, Gustafson-Kessel (GK) clustering algorithm, Gath-Geva (GG) clustering algorithm, Chaira's intuitionistic fuzzy c-means (IFCM), and kernel-based fuzzy c-means with Gaussian kernel functions [KFCM(G)] in standard measurement indexes.

A novel cost function based on decomposing least‐square support vector machine for Takagi–Sugeno fuzzy system identification

This study develops and illustrates a novel cost function which is the combination of these terms constituted of decomposing least square-support vector machine (LS-SVM) and error terms for the identification of Takagi-Sugeno (T-S) fuzzy system based only on measured data without any prior knowledge. The proposed method combines the advantages of fuzzy system theory and some ideas from LS-SVM. Firstly, Gustafson-Kessel clustering algorithm is applied to split training data into R clustering subsets. Likewise, LS-SVM is also decomposed into R terms and consequent parameters a k for T-S fuzzy system corresponding to these terms obtained by decomposing LS-SVM are combined with the error terms to form the new total cost function. Following that, this constrained optimisation problem based on the total cost function can be solved by applying the Lagrange technique. The resulting fuzzy system generated by this method has the following distinct features: (i) the obtained new cost function can be regarded as a structural risk instead of empirical risk; (ii) although incorporating LS-SVM concept into the cost function of T-S fuzzy model, it is shown that the computation process cannot only avoid the selection of kernel function, but also merely use the scalar product for original input space to further reduce the calculation greatly; and (iii) as seen in the proposed novel cost function, the approach can well guarantee the performance of both local-regression models and global model. Finally, the viability and superiority of the method are verified by simulation.

Self-tuning of 2 DOF control based on evolving fuzzy model

In this paper we present a self-tuning of two degrees-of-freedom control algorithm that is designed for use on a non-linear single-input single-output system. The control algorithm is developed based on the Takagi-Sugeno fuzzy model, and it consists of two loops: a feedforward loop and feedback loop. The feedforward part of the controller should drive the system output to the vicinity of the reference signal. It is developed from the inversion of the T-S fuzzy model. To achieve accurate error-free reference tracking a feedback part of the controller is added. A time-varying error-model predictive controller is used in the feedback loop. The error-model is obtained from the T-S fuzzy model. The T-S fuzzy model of the system, required in the controller, is obtained with evolving fuzzy modelling, which is based on recursive Gustafson-Kessel clustering algorithm and recursive fuzzy least squares. It employs evolving mechanisms for adding, removing, merging and splitting the clusters.The presented control approach was experimentally validated on a non-linear second-order SISO system helio-crane in simulation and real environment. Several criteria functions were defined to evaluate the reference-tracking and disturbance rejection performance of the control algorithm. The presented control approach was compared to another fuzzy control algorithm. The experimental results confirm the applicability of the approach.

Clustering Accuracies on Concepts of Nursing

The purpose of this study is to integrate fuzzy clustering algorithm based on common Mahalanobis distance. Fuzzy clustering could distinguish characteristics of concept structures on nursing. Finally, some limitations and suggestions of this study are discussed. It shows that knowledge structures will be feasible for remedial instruction and help students get more chance to get professional certification on nurses. Based on the findings and results, combined with fuzzy clustering algorithm based on normalized Mahalanobis distance could be very feasible for cognition diagnosis in the future. The algorithm provided by Bezdek (1) is used in this study. Fuzzy c-mean algorithm based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. Gustafson-Kessel clustering algorithm needs added constraint of fuzzy covariance matrix, whereas Gath-Geva clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was used. However, the added fuzzy covariance matrices in their distance measure were not directly derived from the objective function. The fuzzy covariance matrices in the Mahalanobis distance can be directly derived by minimizing the objective function. II. LITERATURE REVIEW FCM can only work well for spherical shaped clusters. In the objective function the distances between data points to the centers of the clusters are calculated by Euclidian distances. To overcome the above drawback, we could try to extend the distance measure to Mahalanobis distance (MD). However, Krishnapuram and Kim (1999) pointed out that the Mahalanobis distance can not be used directly in clustering algorithm. Gustafson and Kessel (1979) extended the Euclidian distances of the standard FCM by employing an adaptive norm, in order to detect clusters of different geometrical shape without changing the clusters' sizes in one data set. Gath-Geva (GG) fuzzy clustering algorithm is an extension of Gustafson-Kessel (GK) fuzzy clustering algorithm, and also takes the size and density of clusters for classification. Hence, it has better behaviors for irregular features. For improving the limitation of GK algorithm and GG algorithm, we added a regulating factor of covariance matrix to each class in the objective function, and deleted the constraint of the determinant of covariance matrices. We can obtain the Fuzzy C-Means based on adaptive Mahalanobis distance (FCM-M) as following (2, 3). For improving the stability of the clustering results, we replace all of the covariance matrices with the same common covariance matrix in the objective function in the FCM-M algorithm, and then, an improved fuzzy clustering method, called the Fuzzy C-Means algorithm based on common Mahalanobis distance (FCM-CM) is proposed.

A Classification Algorithm for Network Traffic based on Improved Support Vector Machine

An algorithm to classify the network traffic based on improved support vector machine (SVM) is presented in this paper. Each feature of the traditional support vector machine (SVM) algorithm has the same effect on classification rather than considering its practical effect. To improve the classification accuracy of SVM, the probabilistic distributing area of a feature in a kind of network traffic is obtained from the real network traffic. Then the overlapped degree of the feature’s probabilistic distributing area between two different kinds of network traffic is calculated to obtain the feature’s contribution degree, and the corresponding weight value of the feature is derived from its contribution degree. Thus each feature has different effect on the classification according to its weight value. Considering the feature’s probabilistic distributing area is affected by the outliers or noises intensively, the data space is mapped to high dimension feature space, and the Gustafson-Kessel clustering algorithm is employed to deal with the outliers or noises existing in the input samples. The experimental results show that the method presented in this paper has a higher classification accuracy.