Extending twin support vector machine classifier for multi-category classification problems

© 2013 – IOS Press and the authors. All rights reservedTwin support vector machine classifier (TWSVM) was proposed by Jayadeva et al., which was used for binary classification problems. TWSVM not only overcomes the difficulties in handling the problem of exemplar unbalance in binary classification problems, but also it is four times faster in training a classifier than classical support vector machines. This paper proposes one-versus-all twin support vector machine classifiers (OVA-TWSVM) for multi-category classification problems by utilizing the strengths of TWSVM. OVA-TWSVM extends TWSVM to solve k-category classification problems by developing k TWSVM where in the ith TWSVM, we only solve the Quadratic Programming Problems (QPPs) for the ith class, and get the ith nonparallel hyperplane corresponding to the ith class data. OVA-TWSVM uses the well known one-versus-all (OVA) approach to construct a corresponding twin support vector machine classifier. We analyze the efficiency of the OVA-TWSVM theoretically, and perform experiments to test its efficiency on both synthetic data sets and several benchmark data sets from the UCI machine learning repository. Both the theoretical analysis and experimental results demonstrate that OVA-TWSVM can outperform the traditional OVA-SVMs classifier. Further experimental comparisons with other multiclass classifiers demonstrated that comparable performance could be achieved.This work is supported in part by the grant of the Fundamental Research Funds for the Central Universities of GK201102007 in PR China, and is also supported by Natural Science Basis Research Plan in Shaanxi Province of China (Program No.2010JM3004), and is at the same time supported by Chinese Academy of Sciences under the Innovative Group Overseas Partnership Grant as well as Natural Science Foundation of China Major International Joint Research Project (NO.71110107026)

Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases

This paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, as search strategies, and the generalized F-score (GF) to evaluate the importance of each feature. The new accuracy measure is used as the criterion to evaluated the performance of a temporary SVM to direct the feature selection algorithms. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. To get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. We repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. Experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. Furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases

Escherichia coli of sequence type 3835 carrying blaNDM-1, blaCTX-M-15, blaCMY-42 and blaSHV-12

New Delhi metallo-β-lactamase (NDM) represents a serious challenge for treatment and public health. A carbapenem-resistant Escherichia coli clinical strain WCHEC13-8 was subjected to antimicrobial susceptibility tests, whole genome sequencing and conjugation experiments. It was resistant to imipenem (MIC, >256 μg/ml) and meropenem (MIC, 128 μg/ml) and belonged to ST3835. blaNDM-1 was the only carbapenemase gene detected. Strain WCHEC13-8 also had a plasmid-borne AmpC gene (blaCMY-42) and two extended-spectrum β-lactamase genes (blaCTX-M-15 and blaSHV-12). blaNDM-1 and blaSHV-12 were carried by a 54-kb IncX3 self-transmissible plasmid, which is identical to plasmid pNDM-HF727 from Enterobacter cloacae. blaCMY-42 was carried by a 64-kb IncI1 plasmid and blaCTX-M-15 was located on a 141-kb plasmid with multiple F replicons (replicon type: F36:A4:B1). blaCMY-42 was in a complicated context and the mobilisation of blaCMY-42 was due to the transposition of IS Ecp1 by misidentifying its right-end boundary. Genetic context of blaNDM-1 in strain WCHEC13-8 was closely related to those on IncX3 plasmids in various Enterobacteriaceae species in China. In conclusion, a multidrug-resistant ST3835 E. coli clinical strain carrying blaNDM-1, blaCTX-M-15, blaCMY-42 and blaSHV-12 was identified. IncX3 plasmids may be making a significant contribution to the dissemination of blaNDM among Enterobacteriaceae in China

Comparisons and Applications of Four Independent Numerical Approaches for Linear Gyrokinetic Drift Modes

To help reveal the complete picture of linear kinetic drift modes, four independent numerical approaches, based on integral equation, Euler initial value simulation, Euler matrix eigenvalue solution and Lagrangian particle simulation, respectively, are used to solve the linear gyrokinetic electrostatic drift modes equation in Z-pinch with slab simplification and in tokamak with ballooning space coordinate. We identify that these approaches can yield the same solution with the difference smaller than 1\%, and the discrepancies mainly come from the numerical convergence, which is the first detailed benchmark of four independent numerical approaches for gyrokinetic linear drift modes. Using these approaches, we find that the entropy mode and interchange mode are on the same branch in Z-pinch, and the entropy mode can have both electron and ion branches. And, at strong gradient, more than one eigenstate of the ion temperature gradient mode (ITG) can be unstable and the most unstable one can be on non-ground eigenstates. The propagation of ITGs from ion to electron diamagnetic direction at strong gradient is also observed, which implies that the propagation direction is not a decisive criterion for the experimental diagnosis of turbulent mode at the edge plasmas.Comment: 12 pages, 10 figures, accept by Physics of Plasma