85,750 research outputs found
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
Using an interaction extracted from the local hidden gauge Lagrangians, which
brings together vector and pseudoscalar mesons, and the coupled channels (s-wave), (d-wave), (s-wave) and (d-wave),
we look in the region of MeV and we find two resonances
dynamically generated by the interaction of these channels, which are naturally
associated to the and . The appears neatly as a pole in the complex plane. The free parameters of
the theory are chosen to fit the (d-wave) data. Both the real and
imaginary parts of the 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 resonance, the second scalar
meson after the in the (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 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)
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