49,228 research outputs found
Extending twin support vector machine classifier for multi-category classification problems
Ā© 2013 ā 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
Spin-current diode with a ferromagnetic semiconductor
Diode is a key device in electronics: the charge current can flow through the
device under a forward bias, while almost no current flows under a reverse
bias. Here we propose a corresponding device in spintronics: the spin-current
diode, in which the forward spin current is large but the reversed one is
negligible. We show that the lead/ferromagnetic quantum dot/lead system and the
lead/ferromagnetic semiconductor/lead junction can work as spin-current diodes.
The spin-current diode, a low dissipation device, may have important
applications in spintronics, as the conventional charge-current diode does in
electronics.Comment: 5 pages, 3 figure
The spin-polarized state of graphene: a spin superconductor
We study the spin-polarized Landau-level state of graphene. Due to
the electron-hole attractive interaction, electrons and holes can bound into
pairs. These pairs can then condense into a spin-triplet superfluid ground
state: a spin superconductor state. In this state, a gap opens up in the edge
bands as well as in the bulk bands, thus it is a charge insulator, but it can
carry the spin current without dissipation. These results can well explain the
insulating behavior of the spin-polarized state in the recent
experiments.Comment: 6 pages, 4 figure
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