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

A Framework for Life Cycle Sustainability Assessment of Road Salt Used in Winter Maintenance Operations

It is important to assess from a holistic perspective the sustainability of road salt widely used in winter road maintenance (WRM) operations. The importance becomes increasingly apparent in light of competing priorities faced by roadway agencies, the need for collaborative decision-making, and growing concerns over the risks that road salt poses for motor vehicles, transportation infrastructure, and the natural environment. This project introduces the concept of Life Cycle Sustainability Assessment (LCSA), which combines Life Cycle Costing, Environmental Life Cycle Assessment, and Social Life Cycle Assessment. The combination captures the features of three pillars in sustainability: economic development, environmental preservation, and social progress. With this framework, it is possible to enable more informed and balanced decisions by considering the entire life cycle of road salt and accounting for the indirect impacts of applying road salt for snow and ice control. This project proposes a LCSA framework of road salt, which examines the three branches of LCSA, their relationships in the integrated framework, and the complexities and caveats in the LCSA. While this framework is a first step in the right direction, we envision that it will be improved and enriched by continued research and may serve as a template for the LCSA of other WRM products, technologies, and practices

False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments

Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose statistical methods that can systematically and accurately identify Heterogeneous Treatment Effect (HTE) of any user cohort of interest (e.g. mobile device type, country), and determine which factors (e.g. age, gender) of users contribute to the heterogeneity of the treatment effect in an A/B test. By applying these methods on both simulation data and real-world experimentation data, we show how they work robustly with controlled low False Discover Rate (FDR), and at the same time, provides us with useful insights about the heterogeneity of identified user groups. We have deployed a toolkit based on these methods, and have used it to measure the Heterogeneous Treatment Effect of many A/B tests at Snap

Bulk-edge correspondence, spectral flow and Atiyah-Patodi-Singer theorem for the Z2-invariant in topological insulators

We study the bulk-edge correspondence in topological insulators by taking Fu-Kane spin pumping model as an example. We show that the Kane-Mele invariant in this model is Z2 invariant modulo the spectral flow of a single-parameter family of 1+1-dimensional Dirac operators with a global boundary condition induced by the Kramers degeneracy of the system. This spectral flow is defined as an integer which counts the difference between the number of eigenvalues of the Dirac operator family that flow from negative to non-negative and the number of eigenvalues that flow from non-negative to negative. Since the bulk states of the insulator are completely gapped and the ground state is assumed being no more degenerate except the Kramers, they do not contribute to the spectral flow and only edge states contribute to. The parity of the number of the Kramers pairs of gapless edge states is exactly the same as that of the spectral flow. This reveals the origin of the edge-bulk correspondence, i.e., why the edge states can be used to characterize the topological insulators. Furthermore, the spectral flow is related to the reduced eta-invariant and thus counts both the discrete ground state degeneracy and the continuous gapless excitations, which distinguishes the topological insulator from the conventional band insulator even if the edge states open a gap due to a strong interaction between edge modes. We emphasize that these results are also valid even for a weak disordered and/or weak interacting system. The higher spectral flow to categorize the higher-dimensional topological insulators are expected.Comment: 9 page, accepted for publication in Nucl Phys

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)