11,710 research outputs found
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)
Fuzzy Least Squares Twin Support Vector Machines
Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an
efficient and fast algorithm for binary classification. It combines the
operating principles of Least Squares SVM (LS-SVM) and Twin SVM (T-SVM); it
constructs two non-parallel hyperplanes (as in T-SVM) by solving two systems of
linear equations (as in LS-SVM). Despite its efficiency, LST-SVM is still
unable to cope with two features of real-world problems. First, in many
real-world applications, labels of samples are not deterministic; they come
naturally with their associated membership degrees. Second, samples in
real-world applications may not be equally important and their importance
degrees affect the classification. In this paper, we propose Fuzzy LST-SVM
(FLST-SVM) to deal with these two characteristics of real-world data. Two
models are introduced for FLST-SVM: the first model builds up crisp hyperplanes
using training samples and their corresponding membership degrees. The second
model, on the other hand, constructs fuzzy hyperplanes using training samples
and their membership degrees. Numerical evaluation of the proposed method with
synthetic and real datasets demonstrate significant improvement in the
classification accuracy of FLST-SVM when compared to well-known existing
versions of SVM
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Experimental Investigation of the Transient Flow in Roots Blower
Rotary positive displacement machines are common method to pump flow in various process industries. Their performance highly depends on the operational clearances. It is widely believed that computational fluid dynamics (CFD) can help understanding and reducing internal leakage flows. However, Developments of grid generating tools for use of CFD in rotary positive displacement machines have not yet been fully validated. Thereby arising a need to validate these models that help in better understanding of the leakage flows. Roots blower is a good representative of positive displacement machines and as such is convenient for optical access to analyse flows in in such machines. This paper describes the setup of the experimental test rig with the optical Roots blower in the Centre for Compressor Technology at City, University of London and the first results obtained using three different flow visualization methods. These are namely i) the high-speed camera (HC), ii) the continuous time resolved PIV (CPIV) and iii) the instantaneous PIV obtained with double pulse PIV laser and double shutter camera (IPIV). Test results from these three tests are compared and discussed in the paper. The CPIV test shows the movement of the vortex and the general shape of the flow field clearly but is not sufficient to calculate velocity vectors of high-velocity particles due to the limitation of the laser and camera. The IPIV test can produce quantitative velocity vector images of the internal flow but needs improvement to look into the leakage flow. The work described in this paper is a part of the large project set to evaluate characteristics of the internal flow in rotary positive displacement machines and to characterize leakage flows. The objective is to enable further improvements in 3D CFD analysis of leakage flows in rotary positive displacement machines and ultimately lead to the improvement in the performance of rotary positive displacement machines
Applicability of semi-supervised learning assumptions for gene ontology terms prediction
Gene Ontology (GO) is one of the most important resources in bioinformatics, aiming to provide a unified framework for the biological annotation of genes and proteins across all species. Predicting GO terms is an essential task for bioinformatics, but the number of available labelled proteins is in several cases insufficient for training reliable machine learning classifiers. Semi-supervised learning methods arise as a powerful solution that explodes the information contained in unlabelled data in order to improve the estimations of traditional supervised approaches. However, semi-supervised learning methods have to make strong assumptions about the nature of the training data and thus, the performance of the predictor is highly dependent on these assumptions. This paper presents an analysis of the applicability of semi-supervised learning assumptions over the specific task of GO terms prediction, focused on providing judgment elements that allow choosing the most suitable tools for specific GO terms. The results show that semi-supervised approaches significantly outperform the traditional supervised methods and that the highest performances are reached when applying the cluster assumption. Besides, it is experimentally demonstrated that cluster and manifold assumptions are complimentary to each other and an analysis of which GO terms can be more prone to be correctly predicted with each assumption, is provided.Postprint (published version
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Software Testbed for Selective Laser Sintering
Computer software plays an important role in the implementation of Solid Freeform
Fabrication (SFF) technologies. This paper describes a software testbed for processing
part geometry for a particular SFF technology, selective laser sintering (SLS), that is built
around the separation of the slicing and rasterization operations to accommodate geometric
information from a variety of sources. The paper also discusses the process control
software being developed for a new high-temperat rkstation for SLS of metal
powders. This program features a high-resolution data rmat, the ability to interpolate
to achieve a desired resolution, and a menu-driven user interface with graphical feedback
and process simulation capabilities.Mechanical Engineerin
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