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ROC-boosting: A Feature Selection Method for Health Identification Using Tongue Image

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015年第1期2015卷 1-8页

作者： Cui, Yan Liao, Shizhong Wang, Hongwu Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Tianjin Univ Tradit Chinese Med Dept Common Required Courses Tianjin 300193 Peoples R China Tianjin Univ Tradit Chinese Med Coll Tradit Chinese Med Tianjin 300193 Peoples R China

Objective. To select significant Haar-like features extracted from tongue images for health identification. Materials and Methods. 1,322 tongue cases were included in this study. Health information and tongue images of each case were collected. Cases were classified into the following groups: group containing 148 cases diagnosed as health;group containing 332 cases diagnosed as ill based on health information, even though tongue image is normal;and group containing 842 cases diagnosed as ill. Haar-like features were extracted from tongue images. Then, we proposed a new boosting method in the ROC space for selecting significant features from the features extracted from these images. Results. A total of 27 features were obtained from groups A, B, and C. Seven features were selected from groups A and B, while 25 features were selected from groups A and C. Conclusions. The selected features in this study were mainly obtained from the root, top, and side areas of the tongue. This is consistent with the tongue partitions employed in traditional Chinese medicine. These results provide scientific evidence to TCM tongue diagnosis for health identification.

关键词： ANATOMY Tongue diseases -- Diagnosis Medical informatics Chinese medicine Acquisition of data boosting algorithms Haar system (Mathematics) Tongue

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boosting Terahertz Generation in Laser-Field Ionized Gases Using a Sawtooth Wave Shape

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Physical Review Letters 2015年第18期114卷 183901-183901页

作者： P. González de Alaiza Martínez I. Babushkin L. Bergé S. Skupin E. Cabrera-Granado C. Köhler U. Morgner A. Husakou J. Herrmann CEA-DAM DIF F-91297 Arpajon France Institute of Quantum Optics Leibniz University Hannover Welfengarten 1 30167 Hannover Germany Univ. Bordeaux - CNRS - CEA Centre Lasers Intenses et Applications UMR 5107 33405 Talence France Facultad de Óptica y Optometría Universidad Complutense de Madrid 28037 Madrid Spain Institute for Scientific Computing TU Dresden 01062 Dresden Germany Max-Born-Institut für Nichtlineare Optik und Kurzzeitspektroskopie 12489 Berlin Germany

Broadband ultrashort terahertz (THz) pulses can be produced using plasma generation in a noble gas ionized by femtosecond two-color pulses. Here we demonstrate that, by using multiple-frequency laser pulses, one can obtain a waveform which optimizes the free electron trajectories in such a way that they acquire the largest drift velocity. This allows us to increase the THz conversion efficiency to 2%, an unprecedented performance for THz generation in gases. In addition to the analytical study of THz generation using a local current model, we perform comprehensive 3D simulations accounting for propagation effects which confirm this prediction. Our results show that THz conversion via tunnel ionization can be greatly improved with well-designed multicolor pulses.

关键词： boosting algorithms TERAHERTZ materials IONIZED gases SAWTOOTH generators PLASMA production FEMTOSECOND pulses

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Measuring the Scale of Segregation Using k-Nearest Neighbor Aggregates

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GEOGRAPHICAL ANALYSIS 2015年第1期47卷 34-49页

作者： Osth, John Clark, William A. V. Malmberg, Bo Uppsala Univ Dept Social & Econ Geog SE-75120 Uppsala Sweden Univ Calif Los Angeles Los Angeles CA USA Stockholm Univ Dept Human Geog S-10691 Stockholm Sweden

Nearly all segregation measures use some form of administrative unit (usually tracts in the United States) as the base for the calculation of segregation indices, and most of the commonly used measures are aspatial. The spatial measures that have been proposed are often not easily computed, although there have been significant advances in the past decade. We provide a measure that is individually based (either persons or very small administrative units) and a technique for constructing neighborhoods that does not require administrative units. We show that the spatial distribution of different population groups within an urban area can be efficiently analyzed with segregation measures that use population count-based definitions of neighborhood scale. We provide a variant of a k-nearest neighbor approach and a statistic spatial isolation and a methodology (EquiPop) to map, graph, and evaluate the likelihood of individuals meeting other similar race individuals or of meeting individuals of a different ethnicity. The usefulness of this approach is demonstrated in an application of the method to data for Los Angeles and three metropolitan areas in Sweden. This comparative approach is important as we wish to show how the technique can be used across different cultural contexts. The analysis shows how the scale (very small neighborhoods, larger communities, or cities) influences the segregation outcomes. Even if microscale segregation is strong, there may still be much more mixing at macroscales.

关键词： K-nearest neighbor classification RESEARCH SUPERVISED learning (Machine learning) MACHINE learning boosting algorithms

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Application of boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015年第1期2015卷 1-9页

作者： Shin, Yoonseok Kyonggi Univ Dept Plant & Architectural Engn Suwon 443760 Gyeonggi Do South Korea

Among the recent data mining techniques available, the boosting approach has attracted a great deal of attention because of its effective learning algorithm and strong boundaries in terms of its generalization performance. However, the boosting approach has yet to be used in regression problems within the construction domain, including cost estimations, but has been actively utilized in other domains. Therefore, a boosting regression tree (BRT) is applied to cost estimations at the early stage of a construction project to examine the applicability of the boosting approach to a regression problem within the construction domain. To evaluate the performance of the BRT model, its performance was compared with that of a neural network (NN) model, which has been proven to have a high performance in cost estimation domains. The BRT model has shown results similar to those of NN model using 234 actual cost datasets of a building construction project. In addition, the BRT model can provide additional information such as the importance plot and structure model, which can support estimators in comprehending the decision making process. Consequently, the boosting approach has potential applicability in preliminary cost estimations in a building construction project.

关键词： boosting algorithms REGRESSION trees BUILDING DATA mining MACHINE learning

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Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015年第1期2015卷 1-10页

作者： Nanni, Loris Brahnam, Sheryl Ghidoni, Stefano Lumini, Alessandra Univ Padua DEI I-35131 Padua Italy Missouri State Univ Comp Informat Syst Springfield MO 65804 USA Univ Bologna DISI I-47521 Cesena Italy

We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, randomsubspace of adaboost, randomsubspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset.

关键词： PATTERN perception SUPPORT vector machines HETEROGENEOUS computing GAUSSIAN processes KERNEL functions boosting algorithms

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People and Mobile Robot Classification Through Spatio-Temporal Analysis of Optical Flow

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2015年第6期29卷 1550021-1550021页

作者： Moreno, Plinio Figueira, Dario Bernardino, Alexandre Santos-Victor, Jose Univ Lisbon Inst Super Tecn LARSyS Inst Syst & Robot ISR IST P-1699 Lisbon Portugal

The goal of this work is to distinguish between humans and robots in a mixed human-robot environment. We analyze the spatio-temporal patterns of optical flow-based features along several frames. We consider the Histogram of Optical Flow (HOF) and the Motion Boundary Histogram (MBH) features, which have shown good results on people detection. The spatio-temporal patterns are composed of groups of feature components that have similar values on previous frames. The groups of features are fed into the FuzzyBoost algorithm, which at each round selects the spatio-temporal pattern (i.e. feature set) having the lowest classification error. The search for patterns is guided by grouping feature dimensions, considering three algorithms: (a) similarity of weights from dimensionality reduction matrices, (b) Boost Feature Subset Selection (BFSS) and (c) Sequential Floating Feature Selection (SFSS), which avoid the brute force approach. The similarity weights are computed by the Multiple Metric Learning for large Margin Nearest Neighbor (MMLMNN), a linear dimensionality algorithm that provides a type of Mahalanobis metric Weinberger and Saul, J. MaCh. Learn. Res. 10 (2009) 207-244. The experiments show that FuzzyBoost brings good generalization properties, better than the GentleBoost, the Support Vector Machines (SVM) with linear kernels and SVM with Radial Basis Function (RBF) kernels. The classifier was implemented and tested in a real-time, multi-camera dynamic setting.

关键词： Human robot environments boosting algorithms people versus robot detection optical flow-based features

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boosting Higgs CP properties via VH production at the Large Hadron Collider

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PHYSICS LETTERS B 2014年 730卷 275-279页

作者： Godbole, Rohini Miller, David J. Mohan, Kirtimaan White, Chris D. Indian Inst Sci Ctr High Energy Phys Bangalore 560012 Karnataka India Univ Glasgow Sch Phys & Astron SUPA Glasgow G12 8QQ Lanark Scotland Univ Savoie CNRS LAPTh F-74941 Annecy Le Vieux France

We consider ZH and WH production at the Large Hadron Collider, where the Higgs decays to a b (b) over bar pair. We use jet substructure techniques to reconstruct the Higgs boson and construct angular observables involving leptonic decay products of the vector bosons. These efficiently discriminate between the tensor structure of the HVV vertex expected in the Standard Model and that arising from possible new physics, as quantified by higher dimensional operators. This can then be used to examine the CP nature of the Higgs as well as CP mixing effects in the HZZ and HWW vertices separately. (C) 2014 Elsevier B.V.

关键词： boosting algorithms HIGGS bosons LARGE Hadron Collider (France & Switzerland) RADIOACTIVE decay JETS (Nuclear physics) ASTRONOMICAL observations LEPTONS (Nuclear physics) -- Decay

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A data-adaptive strategy for inverse weighted estimation of causal effects

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HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2014年第3期14卷 69-91页

作者： Zhu, Yeying Ghosh, Debashis Mitra, Nandita Mukherjee, Bhramar Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON N2L 3G1 Canada Penn State Univ Dept Stat University Pk PA 16802 USA Univ Penn Dept Biostat & Epidemiol Philadelphia PA 19104 USA Univ Michigan Dept Biostat Ann Arbor MI 48109 USA

In most nonrandomized observational studies, differences between treatment groups may arise not only due to the treatment but also because of the effect of confounders. Therefore, causal inference regarding the treatment effect is not as straightforward as in a randomized trial. To adjust for confounding due to measured covariates, the average treatment effect is often estimated by using propensity scores. Typically, propensity scores are estimated by logistic regression. More recent suggestions have been to employ nonparametric classification algorithms from machine learning. In this article, we propose a weighted estimator combining parametric and nonparametric models. Some theoretical results regarding consistency of the procedure are given. Simulation studies are used to assess the performance of the newly proposed methods relative to existing methods, and a data analysis example from the Surveillance, Epidemiology and End Results database is presented.

关键词： boosting algorithms Causal inference Logistic regression Observational data Random forests

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SVM based Ensemble Learning for Spatial and Temporal Air Pollution Analysis

SVM based Ensemble Learning for Spatial and Temporal Air Pol...

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2014 International Conference on Information Technology and Management Engineering(ITME 2014)

作者： Shahid ALI Anthony LAI Department of Computing Unitec Department of Electrotechnology Unitec

The paper aims to propose spatial and temporal air pollution data analysis by using Support Vector Machine(SVM) ensemble method along with single SVM, Bagging and Adaboost M1 algorithms to identify three objectives of our research. First, to ascertain a computation method that could handle large amount of spatial and temporal air pollution data. Secondly, to discover a high percentage of classification accuracy of proposed method by deploying ensemble algorithms, i.e. single SVM, Bagging and boosting algorithms. Thirdly, to find best speed of classification of proposed method in terms of computation time. Finally, we compare the ensemble model accuracy of single SVM, Bagging and Adaboost M1 by using Weka 3.6.5 software in order to choose best model to classify air pollution data with high accuracy.

关键词： Single SVM Bagging boosting algorithms

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Data weighing mechanisms for clustering ensembles

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COMPUTERS & ELECTRICAL ENGINEERING 2013年第5期39卷 1433-1450页

作者： Parvin, Hamid Minaei-Bidgoli, Behrouz Alinejad-Rokny, Hamid Punch, William F. Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Univ New S Wales Fac Med Ctr Vasc Res Complex Syst Biol Grp Sydney NSW Australia Univ New S Wales Sch Comp Sci & Engn Sydney NSW Australia Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Inspired by bagging and boosting algorithms in classification, the non-weighing and weighing-based sampling approaches for clustering are proposed and studied in the paper. The effectiveness of non-weighing-based sampling technique, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms have been invested in this paper. Experimental results have shown improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and boosting techniques. Subsamples of small size can reduce the computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical research study also compares the performance of boosting and bagging clustering ensembles using different consensus functions on a number of datasets. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： DATA analysis boosting algorithms PERFORMANCE FUNCTIONAL analysis NUMBER theory EMPIRICAL research DATA mining COMPUTER algorithms

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