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

Environmental Activism, Social Networks and the Internet

Social networks and the internet both have a substantial individual effect on environmental activism in China. In this article, we speculate that social linking patterns between environmental actors, which often facilitate activism on the ground, may also exist in cyberspace in the form of an online network. The article addresses the following empirical questions. Does such an online network exist? If so, who are the constituent actors? Are these the same actors observed on the ground? In addressing these questions the article aims to contribute to the growing debate on the implications of the internet for the potential emergence of social movements in China

A case in favor of the N∗(1700)(3/2−)N^*(1700)(3/2^-)

Using an interaction extracted from the local hidden gauge Lagrangians, which brings together vector and pseudoscalar mesons, and the coupled channels $\rho N$ (s-wave), $\pi N$ (d-wave), $\pi \Delta$ (s-wave) and $\pi \Delta$ (d-wave), we look in the region of $\sqrt s =1400-1850$ MeV and we find two resonances dynamically generated by the interaction of these channels, which are naturally associated to the $N^*(1520) (3/2^-)$ and $N^*(1700) (3/2^-)$. The $N^*(1700) (3/2^-)$ appears neatly as a pole in the complex plane. The free parameters of the theory are chosen to fit the $\pi N$ (d-wave) data. Both the real and imaginary parts of the $\pi N$ amplitude vanish in our approach in the vicinity of this resonance, similarly to what happens in experimental determinations, what makes this signal very weak in this channel. This feature could explain why this resonance does not show up in some experimental analyses, but the situation is analogous to that of the $f_0(980)$ resonance, the second scalar meson after the $\sigma (f_0(500))$ in the $\pi \pi$(d-wave) amplitude. The unitary coupled channel approach followed here, in connection with the experimental data, leads automatically to a pole in the 1700 MeV region and makes this second $3/2^-$ resonance unavoidable

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)