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Probing for Sparse and Fast Variable Selection with Model-Based boosting

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017年第1期2017卷 1-8页

作者： Thomas, Janek Hepp, Tobias Mayr, Andreas Bischl, Bernd LMU Munchen Dept Stat Munich Germany FAU Erlangen Nurnberg Dept Med Informat Biometry & Epidemiol Erlangen Germany Univ Hosp Bonn Dept Med Biometry Informat & Epidemiol Bonn Germany

We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slightly altered data (e.g., cross-validation or bootstrap) to find the optimal number of boosting iterations and prevent overfitting. In our proposed approach, we augment the data set with randomly permuted versions of the true variables, so-called shadow variables, and stop the stepwise fitting as soon as such a variable would be added to the model. This allows variable selection in a single fit of the model without requiring further parameter tuning. We show that our probing approach can compete with state-of-the-art selection methods like stability selection in a high-dimensional classification benchmark and apply it on three gene expression data sets.

关键词： HIGH-dimensional model representation STATISTICAL models (Nuclear physics) GENE expression BOOTSTRAPPING (Statistics) boosting algorithms

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

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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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Imbalanced data preprocessing model for web service classification

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2024年第10期15卷 4825-4837页

作者： Rhmann, Wasiur Ishrat, Amaan Lovely Profess Univ Dept Comp Applicat Jalandhar Punjab India Shri Ramswaroop Mem Univ Dept Comp Sci & Engn Lucknow India

Web services are a novel method of web application development. They allow business to adapt to a new environment and change quickly according to customer needs. The client requires high-quality web services with minimal response time, more security, and high availability. With the increasing demand for web services, the introduction of web services rapidly in the business environment has influenced rapidly the web service quality. In the present work, a novel model for web service classification is proposed. Three metaheuristic techniques: Whale optimization algorithm, Simulated annealing algorithm, and Ant colony optimization are used to select the best subset of features. Web service-based imbalanced dataset is balanced using SMOTETomek (Synthetic minority oversampling + Tomek link). Ensemble Adaboost and Gradient boosting algorithms are used for the creation of a web service prediction model. The publicly available QWS dataset is used for experimental purposes. The results of the proposed models are compared with machine learning techniques. It was observed that the Ant colony algorithm performed best for relevant feature selection and the Ensemble Adaboost and Gradient boosting algorithm outperformed all other machine learning techniques for web service classification.

关键词： Web services Machine learning boosting algorithms Ensemble techniques

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A fast genetic method for inducting descriptive fuzzy models

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FUZZY SETS AND SYSTEMS 2004年第1期141卷 33-46页

作者： Sánchez, L Otero, J Univ Oviedo Dept Informat Gijon 33203 Spain

Under certain inference mechanisms, fuzzy rule bases can be regarded as extended additive models. This relationship can be applied to extend some statistical techniques to learn fuzzy models from data. The interest in this parallelism is twofold: theoretical and practical. First, extended additive models can be estimated by means of the matching pursuit algorithm, which has been related to Support Vector Machines, boosting and Radial Basis neural networks learning;this connection can be exploited to better understand the learning of fuzzy models. In particular, the technique we propose here can be regarded as the counterpart to boosting fuzzy classifiers in the field of fuzzy modeling. Second, since matching pursuit is very efficient in time, we can expect to obtain faster algorithms to learn fuzzy rules from data. We show that the combination of a genetic algorithm and the backfitting process learns faster than ad hoc methods in certain datasets. (C) 2003 Elsevier B.V. All rights reserved.

关键词： descriptive fuzzy models genetic fuzzy systems boosting algorithms backfitting matching pursuit

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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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Unleashing the Potential of boosting Techniques to Optimize Station-Pairs Passenger Flow Forecasting

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Procedia Computer Science 2024年 235卷 32-44页

作者： Madhuri Patel Samir B. Patel Debabrata Swain Siddharth Shah Department of Computer Engineering Pandit Deendayal Energy University Gandhingar India Department of Information Technology L D College of Engineering Ahmedabad India

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient boosting (XGBoost), Light Gradient boosting model (LightGBM), and Categorical boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatch

关键词： Public transport Station Pair passenger flow prediction boosting algorithms LightGBM

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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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Conditional Random Field (CRF)-boosting: Constructing a Robust Online Hybrid boosting Multiple Object Tracker Facilitated by CRF Learning

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SENSORS 2017年第3期17卷 617-617页

作者： Yang, Ehwa Gwak, Jeonghwan Jeon, Moongu Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF) for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable.

关键词： visual sensors multiple object tracking data association conditional random fields boosting algorithms hybrid approaches

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Customer Transaction Prediction System

Customer Transaction Prediction System

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Complex Adaptive Systems Conference (CAS) on Leveraging AI and Machine Learning for Societal Challenges

作者： Jaiswal, Devendra Prakash Kumar, Srishti Mukherjee, Partha Penn State Univ 30 E Swedesford Rd Malvern PA 19355 USA

In this data-driven world, every innovation is targeted towards the attainment of a better future where we can sustain ourselves in the easiest and most comfortable of ways. The desire of this utopia has induced numerous inventions which has led us all to this era where Artificial Intelligence has become a very crucial part of our daily lives. One such implementation which we discuss here, pertains to building a customer transaction predictions system using a completely anonymized dataset. We incorporate deep learning methodologies along with a gradient boosting tree-based algorithm which would help us in identifying the best approach for our problem. This implementation aims to enhance human intuition through techniques, which would be capable of helping the institutions in achieving customer satisfaction and in identifying important features from the anonymized dataset. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Anonymized Dataset Handling Machine Learning Artificial Intelligence Artificial Neural Networks boosting algorithms XGBoost Data Cleaning and Pre-processing

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A Comparative Study of Machine Learning algorithms for Predicting Weight Range of Neonate

A Comparative Study of Machine Learning Algorithms for Predi...

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International Conference on Decision Aid Sciences and Applications (DASA)

作者： Adeeba, Saleem Banujan, Kuhaneswaran Kumara, B. T. G. S. Prasanth, Senthan Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Belihuloya Sri Lanka Sabaragamuwa Univ Sri Lanka Dept Phys Sci & Technol Belihuloya Sri Lanka

ISBN: (纸本)9781665495011

Birth weight is a crucial measure of pregnancy outcome, and it indicates a neonate's chances of longevity, growth, long-lived health, and mental development. In epidemiological studies, it is commonly regarded as the causative pathway to these health consequences. Scanning the fetus's weight before birth is associated with difficulties and is typically calculated using empirical calculations based on doctors' clinical experience. In this research, the authors proposed an approach to predict the neonate weight class with minimum errors and costs during six months of pregnancy, utilising existing pregnant women's data. Authors recognised 16 attributes after an extreme literature survey and expert opinions. The haemoglobin level of the mother, mother's pre-pregnancy weight and height, pregnant woman's weight each month until 6 are some attributes identified. For the Machine learning (ML) models to work, a dataset of 1000 pregnant women was obtained and preprocessed. Then dataset was prepared to apply ML models. This research uses eight different ML algorithms to predict the neonate weight class at six months of pregnancy. Also, this research provides an approach to compare different eight ML models to find an effective algorithm for the neonate weight prediction. The Linear Discriminant Analysis (LDA) model has higher performance than other models. Accuracy, precision, recall and F1-score for LDA are 81.94 %, 83.05%, 94.23% and 88.28 % respectively.

关键词： neonate weight boosting algorithms machine learning

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