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Ensemble Learning for Short-Term Traffic Prediction Based on Gradient boosting Machine

JOURNAL OF SENSORS 2017年第5-4期2017卷 1-15页

作者： Yang, Senyan Wu, Jianping Du, Yiman He, Yingqi Chen, Xu Tsinghua Univ Inst Transportat Engn Dept Civil Engn Beijing 100084 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

Short-term traffic prediction is vital for intelligent traffic systems and influenced by neighboring traffic condition. Gradient boosting decision trees (GBDT), an ensemble learning method, is proposed to make short-term traffic prediction based on the traffic volume data collected by loop detectors on the freeway. Each new simple decision tree is sequentially added and trained with the error of the previous whole ensemble model at each iteration. The relative importance of variables can be quantified in the training process of GBDT, indicating the interaction between input variables and response. The influence of neighboring traffic condition on prediction performance is identified through combining the traffic volume data collected by different upstream and downstream detectors as the input, which can also improve prediction performance. The relative importance of input variables for 15 GBDT models is different, and the impact of upstream traffic condition is not balanced with that of downstream. The prediction accuracy of GBDT is generally higher than SVM and BPNN for different steps ahead, and the accuracy of multi-step-ahead models is lower than 1-step-ahead models. For 1-step-ahead models, the prediction errors of GBDT are smaller than SVM and BPNN for both peak and nonpeak hours.

关键词： Traffic incident management Learning boosting algorithms Acquisition of data Prediction (Logic)

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boosting Thermoelectric Performance by Controlled Defect Chemistry Engineering in Ta-Substituted Strontium Titanate

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CHEMISTRY OF MATERIALS 2015年第14期27卷 4995-5006页

作者： Yaremchenko, Aleksey A. Populoh, Sascha Patricio, Sonia G. Macias, Javier Thiel, Philipp Fagg, Duncan P. Weidenkaff, Anke Frade, Jorge R. Kovalevsky, Andrei V. Univ Aveiro CICECO Aveiro Inst Mat Dept Mat & Ceram Engn P-3810193 Aveiro Portugal Univ Aveiro TEMA NRD Dept Mech Engn AIN P-3810193 Aveiro Portugal Empa Mat Energy Convers CH-8600 Zurich Switzerland Univ Stuttgart Inst Mat Sci Mat Chem DE-70569 Stuttgart Baden Wurttembe Germany

Inspired by recent research results that have demonstrated appealing thermoelectric performance of A-site cation-deficient titanates, this work focuses on detailed analysis of the changes in performance promoted by altering the defect chemistry mechanisms. The series of cation-stoichiometric SrTi1-xTaxO3 +/-delta and A-site deficient Sr1-x/2Ti1-xTaxO3-delta compositions (0.05 <= x <= 0.30) with cubic perovskite-like structure were selected to demonstrate the defect chemistry engineering approaches, which result in promising electric and thermal properties. High power factors were observed in compositions where appropriate concentration of the charge carriers and their mobility were attained by the presence of strontium- and oxygen vacancies and suppressed formation of the oxygen-rich layers. Noticeable deviations from stoichiometric oxygen content were found to decrease the lattice thermal conductivity, suggesting good phonon scattering ability for oxygen vacancies, vacant A-sites, and oxygen-excessive defects, while the effect from donor substitution on the thermal transport was less pronounced. The obtained guidelines for the defect chemistry engineering in donor-substituted strontium titanates open new possibilities for boosting the thermoelectric performance, especially if followed by complementary microstructural design to further promote electrical and thermal transport.

关键词： THERMOELECTRICITY boosting algorithms CRYSTAL defects TANTALUM STRONTIUM titanate THERMOELECTRIC effects

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boosting automatic event extraction from the literature using domain adaptation and coreference resolution

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BIOINFORMATICS 2012年第13期28卷 1759-1765页

作者： Miwa, Makoto Thompson, Paul Ananiadou, Sophia Univ Manchester Natl Ctr Text Min NaCTeM Manchester M1 7DN Lancs England Univ Manchester Sch Comp Sci Manchester M1 7DN Lancs England

Motivation: In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage. Results: We have enhanced our existing EE system, EventMine, in two ways. First, we developed a new coreference resolution (CR) system and integrated it with EventMine. The standalone performance of our CR system in resolving anaphoric references to proteins is considerably higher than the best ranked system in the COREF subtask of the BioNLP'11 Shared Task. Secondly, the improved EventMine incorporates domain adaptation (DA) methods, which extend EE coverage by allowing several different annotated corpora to be used during training. Combined with a novel set of methods to increase the generality and efficiency of EventMine, the integration of both CR and DA have resulted in significant improvements in EE, ranging between 0.5% and 3.4% F-Score. The enhanced EventMine outperforms the highest ranked systems from the BioNLP'09 shared task, and from the GENIA and Infectious Diseases subtasks of the BioNLP'11 shared task.

关键词： boosting algorithms FEATURE extraction (Data processing) BIOINFORMATICS PERFORMANCE evaluation ADAPTATION (Biology) COMMUNICABLE diseases

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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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2015 Marshall Urist Young Investigator Award: Prognostication in Patients With Long Bone Metastases: Does a boosting Algorithm Improve Survival Estimates?

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CLINICAL ORTHOPAEDICS AND RELATED RESEARCH 2015年第10期473卷 3112-3121页

作者： Janssen, Stein J. van der Heijden, Andrea S. van Dijke, Maarten Ready, John E. Raskin, Kevin A. Ferrone, Marco L. Hornicek, Francis J. Schwab, Joseph H. Harvard Univ Massachusetts Gen Hosp Sch Med Dept Orthopaed SurgOrthopaed Oncol Serv Boston MA USA Harvard Univ Brigham & Womens Hosp Sch Med Dept Orthopaed SurgOrthopaed Oncol Serv Boston MA 02115 USA Massachusetts Gen Hosp Boston MA 02114 USA

Survival estimation guides surgical decision-making in metastatic bone disease. Traditionally, classic scoring systems, such as the Bauer score, provide survival estimates based on a summary score of prognostic factors. Identification of new factors might improve the accuracy of these models. Additionally, the use of different algorithms-nomograms or boosting algorithms-could further improve accuracy of prognostication relative to classic scoring systems. A nomogram is an extension of a classic scoring system and generates a more-individualized survival probability based on a patient's set of characteristics using a figure. boosting is a method that automatically trains to classify outcomes by applying classifiers (variables) in a sequential way and subsequently combines them. A boosting algorithm provides survival probabilities based on every possible combination of variables. We wished to (1) assess factors independently associated with decreased survival in patients with metastatic long bone fractures and (2) compare the accuracy of a classic scoring system, nomogram, and boosting algorithms in predicting 30-, 90-, and 365-day survival. We included all 927 patients in our retrospective study who underwent surgery for a metastatic long bone fracture at two institutions between January 1999 and December 2013. We included only the first procedure if patients underwent multiple surgical procedures or had more than one fracture. Median followup was 8 months (interquartile range, 3-25 months);369 of 412 (90%) patients who where alive at 1 year were still in followup. Multivariable Cox regression analysis was used to identify clinical and laboratory factors independently associated with decreased survival. We created a classic scoring system, nomogram, and boosting algorithms based on identified variables. Accuracy of the algorithms was assessed using area under the curve analysis through fivefold cross validation. The following factors were associated with a decreased

关键词： BONE metastasis RESEARCH MEDICAL decision making SURVIVAL analysis (Biometry) boosting algorithms BODY mass index NOMOGRAPHY (Mathematics) FOLLOW-up studies (Medicine)

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Prediction of Load Capacities of Closed-Ended Piles Using boosting Machine Learning Methods 5th

Prediction of Load Capacities of Closed-Ended Piles Using Bo...

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5th International Conference on New Developments in Soil Mechanics and Geotechnical Engineering (ICNDSMGE-ZM)

作者： Karakas, S. Ulker, M. B. C. Taskin, G. Istanbul Univ Cerrahpasa Dept Civil Engn TR-34320 Istanbul Turkiye Istanbul Tech Univ Inst Disaster Management Earthquake Engn Program Istanbul Turkiye

ISBN: (纸本)9783031201745;9783031201721;9783031201714

In this study, a novel data-driven model is developed using boosting-type machine learning algorithms with the aim of predicting the ultimate load-bearing capacities of closed-ended piles. A comprehensive database is gathered using the full-scale load test data with four features. Special boosting type machine learning methods are trained and tested with the database. Once predictions are made, a newly developed machine learning algorithm called Shapley method is utilized to decide the effectiveness of the selected features in predicting pile capacities. Results indicate that the pile cross-section area and length features are sufficient to achieve accurate predictions covering the parameters on the pile side and the CPT-based tip resistance is the only parameter needed on the soil side. While different boosting methods result in different levels of accuracy in predicting the load bearing capacities of closed-ended piles, it is generally possible to determine the minimum number of features necessary to satisfy a high goodness of fit. In the end, optimum number of features are determined in the prediction process using the Shapley method through the boosting algorithms giving us a valuable prediction tool for estimating the bearing capacity of closed-ended piles.

关键词： boosting algorithms Closed-ended piles CPT test Load-bearing capacity Machine learning Shapley method

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Regularised gradient boosting for financial time-series modelling

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COMPUTATIONAL MANAGEMENT SCIENCE 2017年第3期14卷 367-391页

作者： Agapitos, Alexandros Brabazon, Anthony O'Neill, Michael Univ Coll Dublin Sch Comp Sci Dublin Ireland Univ Coll Dublin Sch Business Dublin Ireland

Gradient boosting (GB) learns an additive expansion of simple basis-models. This is accomplished by iteratively fitting an elementarymodel to the negative gradient of a loss function with respect to the expansion's values at each training data-point evaluated at each iteration. For the case of squared-error loss function, the negative gradient takes the form of an ordinary residual for a given training data-point. Studies have demonstrated that running GB for hundreds of iterations can lead to overfitting, while a number of authors showed that by adding noise to the training data, generalisation is impaired even with relatively few basis-models. Regularisation is realised through the shrinkage of every newly-added basis-model to the expansion. This paper demonstrates that GB with shrinkage-based regularisation is still prone to overfitting in noisy datasets. We use a transformation based on a sigmoidal function for reducing the influence of extreme values in the residuals of a GB iteration without removing them from the training set. This extension is built on top of shrinkage-based regularisation. Simulations using synthetic, noisy data show that the proposed method slows-down overfitting and reduces the generalisation error of regularised GB. The proposed method is then applied to the inherently noisy domain of financial time-series modelling. Results suggest that for the majority of datasets the method generalises better when compared against standard regularised GB, as well as against a range of other time-series modelling methods.

关键词： boosting algorithms Gradient boosting Stagewise additive modelling Regularisation Financial time-series modelling Financial forecasting Feedforward neural networks Noisy data Ensemble learning

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Convolution neural network based multi-class classification of rehabilitation exercises for diastasis recti abdominis using wearable EMG-IMU sensors

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ENGINEERING COMPUTATIONS 2024年第10期41卷 2381-2403页

作者： Radhakrishnan, Menaka Premkumar, Vinitha Joshy Prahaladhan, Viswanathan Balasubramanian Mukesh, Baskaran Nithish, Purushothaman Vellore Inst Technol Ctr Cyber Phys Syst Chennai India Vellore Inst Technol Sch Elect Engn Chennai India

PurposeGlobally, postnatal women endure a prominent issue caused by midline separation of abdominal recti muscles, characterized by a sagging and pouch-like appearance of the belly termed as Diastasis Recti Abdominis (DRA). The necessity of ensuring the efficacy of rehabilitative workouts for individuals with DRA cannot be overstated, as inaccurate exercises can exacerbate the condition and deteriorate the health of affected women. The purpose of these exercises is to specifically focus on the rectus abdominis muscles to facilitate the reapproximation of the linea alba. The primary aim of this research work is to assess the effectiveness of rehabilitation exercises for DRA women obtained from Inertial Measurement Unit (IMU) and Electromyography (EMG) ***/methodology/approachConvolutional neural networks (CNN) employs convolutional activation functions and pooling layers. Recently, 1D CNNs have emerged as a promising approach used in various applications, including personalized biomedical data classification and early diagnosis, structural health monitoring and anomaly detection. Yet another significant benefit is the feasibility of a real-time and cost-effective implementation of 1D CNN. The EMG and IMU signals serve as inputs for the 1D CNN. Features are then extracted from the fully connected layer of the CNN and fed into a boosting machine learning algorithm for *** findings demonstrate that a combination of sensors provides more details about the exercises, thereby contributing to the classification *** implicationsIn real time, collecting data from postnatal women was incredibly challenging. The process of examining these women was time-consuming, and they were often preoccupied with their newborns, leading to a reluctance to focus on their own health. Additionally, postnatal women might not be fully aware of the implications of DRA and the importance of rehabilitation exercises. Many might not realize that negle

关键词： Diastasis recti abdominis Electromyography Inertial measurement unit Convolutional neural network boosting algorithms

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A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2022年第2期73卷 301-325页

作者： Zhang, Fan Fleyeh, Hasan Bales, Chris Dalarna Univ Dept Microdata Anal S-79188 Falun Sweden Dalarna Univ Dept Energy Technol Falun Sweden Dalarna Univ Dept Comp Engn Falun Sweden

Electricity price forecasting plays a crucial role in a liberalised electricity market. Generally speaking, long-term electricity price is widely utilised for investment profitability analysis, grid or transmission expansion planning, while medium-term forecasting is important to markets that involve medium-term contracts. Typical applications of medium-term forecasting are risk management, balance sheet calculation, derivative pricing, and bilateral contracting. Short-term electricity price forecasting is essential for market providers to adjust the schedule of production, i.e., balancing consumers' demands and electricity generation. Results from short-term forecasting are utilised by market players to decide the timing of purchasing or selling to maximise profits. Among existing forecasting approaches, neural networks are regarded as the state of art method due to their capability of modelling high non-linearity and complex patterns inside time series data. However, deep neural networks are not studied comprehensively in this field, which represents a good motivation to fill this research gap. In this article, a deep neural network-based hybrid approach is proposed for short-term electricity price forecasting. To be more specific, categorical boosting (Catboost) algorithm is used for feature selection and a bidirectional long short-term memory neural network (BDLSTM) will serve as the main forecasting engine in the proposed method. To evaluate the effectiveness of the proposed approach, 2018 hourly electricity price data from the Nord Pool market are invoked as a case study. Moreover, the performance of the proposed approach is compared with those of multi-layer perception (MLP) neural network, support vector regression (SVR), ensemble tree, ARIMA as well as two recent deep learning-based models, gated recurrent units (GRU) and LSTM models. A real-world dataset of Nord Pool market is used in this study to validate the proposed approach. Mean percentage error (MAP

关键词： Bidirectional long short-term memory neural network deep learning electricity price forecasting machine learning boosting algorithms energy market

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Modelling biochemical oxygen demand in a large inland aquaculture zone of India: Implications and insights

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 906卷 167386页

作者： Nagaraju, T. Vamsi Bala, G. Sri Bonthu, Sridevi Mantena, Sireesha SRKR Engn Coll Dept Civil Engn Bhimavaram India SRKR Engn Coll Ctr Clean & Sustainable Environm Bhimavaram India Vishnu Inst Technol Dept Comp Sci & Engn Kovvada India Andhra Univ Coll Engn Dept Geoengn Visakhapatnam Andhra Pradesh India

Water quality surveillance is tough, and a specific timely management is necessary for the inland aquaculture ponds and ecology as well. Real time quality monitoring involves the study of numerous parameters includes physical (turbidity, temperature, and specific conductivity), chemical (pH, calcium, manganese, chlorides, iron, biochemical oxygen demand), and biological (bacteria and algae). It is also crucial to recognize the interdependence among the parameters. Alternatively, these relationships can be predicted with statistical and numerical modelling. Organic strength parameter 5-day biochemical oxygen demand (BOD) is a significant parameter to evaluate since its impact is very high on the quality of water, aquatic life, and other biological concerns. This study focuses on the prediction of BOD using six traditional and four boosting algorithms considering ten input physicochemical attributes. The attributes were fine-tuned for highly precise predictions by removing extreme values from the data set using data outlier treatment. The prediction results are compared using performance metrics such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). The findings revealed that boosting algorithms outperform the results of traditional models with the highest prediction accuracy. Among the boosting algorithms, eXtreme Gradient boosting algorithm (XGBM) is found highly appropriate for the inland aquaculture waters with R2 =

关键词： Ammonia Aquaculture Water quality BOD boosting algorithms

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