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

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)

Enhancement of Transition Temperature in FexSe0.5Te0.5 Film via Iron Vacancies

The effects of iron deficiency in FexSe0.5Te0.5 thin films (0.8<x<1) on superconductivity and electronic properties have been studied. A significant enhancement of the superconducting transition temperature (TC) up to 21K was observed in the most Fe deficient film (x=0.8). Based on the observed and simulated structural variation results, there is a high possibility that Fe vacancies can be formed in the FexSe0.5Te0.5 films. The enhancement of TC shows a strong relationship with the lattice strain effect induced by Fe vacancies. Importantly, the presence of Fe vacancies alters the charge carrier population by introducing electron charge carriers, with the Fe deficient film showing more metallic behavior than the defect-free film. Our study provides a means to enhance the superconductivity and tune the charge carriers via Fe vacancy, with no reliance on chemical doping.Comment: 15 pages, 4 figure

Functional analysis of the Bunyamwera orthobunyavirus Gc glycoprotein

The virion glycoproteins Gn and Gc of Bunyamwera orthobunyavirus (family Bunyaviridae) are encoded by the M RNA genome segment and have roles in both viral attachment and membrane fusion. To investigate further the structure and function of the Gc protein in viral replication, we generated 12 mutants that contain truncations from the N terminus. The effects of these deletions were analysed with regard to Golgi targeting, low pH-dependent membrane fusion, infectious virus-like particle (VLP) formation and virus infectivity. Our results show that the N-terminal half (453 residues) of the Gc ectodomain (909 residues in total) is dispensable for Golgi trafficking and cell fusion. However, deletions in this region resulted in a significant reduction in VLP formation. Four mutant viruses that contained N-terminal deletions in their Gc proteins were rescued, and found to be attenuated to different degrees in BHK-21 cells. Taken together, our data indicate that the N-terminal half of the Gc ectodomain is dispensable for replication in cell culture, whereas the C-terminal half is required to mediate cell fusion. A model for the domain structure of the Gc ectodomain is proposed