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4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Asadi, Nima Jamzad, Mansour Sajedi, Hedieh Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9780769532783

Powerful universal steganalyzers were proposed in the literature during the past few years. In addition some studies have been conducted on improvements of current steganalysis results using information fusion techniques, merging available feature vectors, etc. This paper presents two independent ideas, which can be used together, to obtain higher accuracy in detecting stego images. First, we propose the use of boosted fusion methods to aggregate outputs of multiple steganalyzers. Second, we investigate how passing high frequencies through filtering can enhance the results of steganalysis techniques. In this work, it is shown that, through different tests over the state-of-the-art steganography algorithms, an increase in detection accuracy is achieved by using the proposed new ideas.

关键词： steganalysis steganography boosting algorithms Gaussian filtering fusion techniques

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A Generic Framework for the Design of Visual-based Gesture Control Interface

A Generic Framework for the Design of Visual-based Gesture C...

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5th IEEE Conference on Industrial Electronics and Applications

作者： Chen, Wen-Hui Lin, Yu-Hong Yang, Shun-Jie Natl Taipei Univ Technol Inst Automat Technol Taipei Taiwan

ISBN: (纸本)9781424450466

Most visual-based gesture control systems are bound to specific applications. They used predefined postures for users to control devices. Users need to learn and be familiar with those predefined postures to issue a command. This makes it difficult to transfer one gesture control interface into different applications. This study presented a generic framework for the design of a human machine interface based on visual-based hand gesture recognition techniques. Experimental results showed that the proposed approach is feasible for practical applications.

关键词： Human machine interface boosting algorithms Finite state machines Gesture control systems

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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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PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine

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BIOINFORMATICS 2018年第7期34卷 1092-1098页

作者： Rawi, Reda Mall, Raghvendra Kunji, Khalid Shen, Chen-Hsiang Kwong, Peter D. Chuang, Gwo-Yu NIAID Vaccine Res Ctr NIH Bethesda MD 20892 USA Hamad Bin Khalifa Univ Qatar Comp Res Inst Doha 34110 Qatar

Motivation: Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought. Results: In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthew's correlation coefficient, with an overall accuracy of 74% and Matthew's correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability.

关键词： PROTEIN solubility boosting algorithms SEQUENCES (Mathematics) CORRELATORS TRIPEPTIDES ACCURACY

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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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Classification of Flood Disaster Risks with the Use of Gradient boosting Algorithm 22

Classification of Flood Disaster Risks with the Use of Gradi...

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Proceedings of the 2022 5th International Conference on Computational Intelligence and Intelligent Systems

作者： John Paul Quilingking Tomas Gabriela Andes Razmin Bernadette Ellazar Ayesha Keith Santos School of Information Technology Mapua University Philippines

ISBN: (纸本)9781450397612

This study used base and ensemble approaches to classify the flood disaster risks in a local provincial capital in the Philippines using an intelligent methodology based on machine learning. It focused on Gradient boosting Algorithm with Decision Trees as base classifiers/estimators. The researchers consulted with experts to determine the weights of causative factors to fluvial flooding, which were then classified into four (4) risk levels using the Quantile Method and the Exponential Regression for missing value imputation. The K-fold cross-validation was used to validate the proposed algorithms. The experiment shows that Gradient boosting Algorithm is the most appropriate model for the disaster data with the score of 80.00%, more than 70% in all the classification criteria (accuracy, precision, recall f1-score), respectively.

关键词： Gradient boosting boosting algorithms Classification

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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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Assessing the performance of state-of-the-art machine learning algorithms for predicting electro-erosion wear in cryogenic treated electrodes of mold steels

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ADVANCED ENGINEERING INFORMATICS 2024年 61卷

作者： Cetin, Abdurrahman Atali, Gokhan Erden, Caner Ozkan, Sinan Serdar Sakarya Univ Appl Sci Vocat Sch Sakarya Machinery & Met Technol Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Mechatron Engn Sakarya Turkiye Sakarya Univ Appl Sci Fac Technol Dept Comp Engn Sakarya Turkiye Sakarya Univ Appl Sci AI Res & Applicat Ctr Sakarya Turkiye

In manufacturing, predicting and reducing electro-erosion wear during the electric discharge machining (EDM) process is critical to minimize delays, financial losses and product defects. Achieving this requires developing and evaluating accurate machine learning models. In our study, we focus on cryogenically treated mold steel electrodes to investigate the potential of different machine learning algorithms to predict EDM wear. We considered five machine learning algorithms-artificial neural networks, ensemble learning, boosting algorithms, tree-based algorithms, and k-nearest neighbors-to evaluate their ability to predict wear patterns accurately. Each algorithm was trained and tested using actual experimental data from EDM processes. Our results show that the machine learning models demonstrated exceptional accuracy, accurately predicting EDM wear in training and testing datasets with almost 99% accuracy. In addition, we identified the most influential characteristics that affect wear patterns, including operating current, cryogenic process parameters, and electrode composition. Based on these findings, manufacturers can gain valuable insight into the factors that cause EDM wear and optimize their EDM processes accordingly to improve productivity, reduce wear-related costs, and increase production quality across multiple manufacturing industries. Furthermore, this research provides insights into the possibilities of implementing these models in real manufacturing contexts and motivates future research on this topic. Ultimately, integrating advanced computing techniques and prudent decision-making strategies will shape the future of manufacturing operations management and promote sustainable and profitable business growth.

关键词： Electro-erosion wear Cryogenic treatment Mold steel Machine learning algorithms boosting algorithms Ensemble learning

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Support vector machines for spam categorization

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1999年第5期10卷 1048-1054页

作者： Drucker, H Wu, DH Vapnik, VN AT&T Bell Labs Res Red Bank NJ 07701 USA Monmouth Univ Dept Elect Engn W Long Branch NJ 07764 USA Rensselaer Polytech Inst Troy NY 12181 USA

We study the use of support vector machines (SVM's) In classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees, These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000, SVM's performed best when using binary features. For both data sets, boosting trees and SVM's had acceptable test performance in terms of accuracy and speed. However, SVM's had significantly less training time.

关键词： boosting algorithms classification e-mail feature representation Ripper Rocchio support vector machines

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Design of concrete-filled steel tubular columns using data-driven methods

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2023年 200卷

作者： Degtyarev, Vitaliy V. Thai, Huu-Tai New Millennium Bldg Syst LLC 3700 Forest Dr Suite 501 Columbia SC 29204 USA Univ Melbourne Dept Infrastruct Engn Parkville Vic 3010 Australia

By leveraging the merits of structural steel and concrete materials, concrete-filled steel tubular (CFST) struc-tures have been increasingly used in the composite construction of bridges and high-rise buildings. However, their design equations are more complicated than those of steel and reinforced concrete (RC) structures, especially for circular columns under eccentric loading. Therefore, the use of emerging data-driven approaches will help structural engineers ease the design process. This paper explores the use of data-driven design methods as alternatives to conventional mechanics-based design models. Five boosting algorithms, including adaptive boosting (AdaBoost), gradient boosting machine (GBR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical gradient boosting (CatBoost), are employed to develop predictive models for four different types of CFST columns (i.e., circular columns, rectangular columns, circular beam-columns, and rectangular beam-columns). These predictive models are trained using the most up-to-date and comprehensive database collected from over 3,200 test specimens. Reliability analysis is conducted to calibrate the resistance reduction factors for three different design frameworks (i.e., the US, Eurocode, and Australian frameworks) to ensure that the newly developed predictive models meet the target reliability indices required by different design frameworks. A web-based design tool is also developed to promote the practical use of data-driven methods for the design of CFST columns.

关键词： CFST columns Machine learning boosting algorithms Structural design Resistance reduction factor Reliability analysis

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