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A Machine Learning-Based Framework for Water Quality Index Estimation in the Southern Bug River

WATER 2023年第20期15卷 3543页

作者： Masood, Adil Niazkar, Majid Zakwan, Mohammad Piraei, Reza Jamia Millia Islamia Dept Civil Engn New Delhi 110025 India Free Univ Bozen Bolzano Fac Engn Piazza Univ 5 I-39100 Bolzano Bolzano Italy Maulana Azad Natl Urdu Univ Sch Technol Hyderabad 500032 Telangana India Shiraz Univ Dept Civil Engn Shiraz *** Iran

River water quality is of utmost importance because the river is not only one of the key water resources but also a natural habitat serving its surrounding environment. In a bid to address whether it has a qualified quality, various analytics are required to be considered, but it is challenging to measure all of them frequently along a river reach. Therefore, estimating water quality index (WQI) incorporating several weighted analytics is a useful approach to assess water quality in rivers. This study explored applications of ten machine learning (ML) models to estimate WQI for the Southern Bug River, which is the second-longest river in Ukraine. The ML methods considered in this study include artificial neural networks (ANNs), Support Vector Regressor (SVR), Extreme Learning Machine, Decision Tree Regressor, random forest, AdaBoost (AB), Gradient boosting Regressor, XGBoost Regressor (XGBR), Gaussian process (GP), and K-nearest neighbors (KNN). Each data measurement consists of nine analytics (NH4, BOD5, suspended solids, DO, NO3, NO2, SO4, PO4, Cl), while the quantity of data is more than 2700 data points. The results indicated that all ML models demonstrate satisfactory performance in predicting WQI. However, GP outperformed the other models, followed by XGBR, SVR, and KNN. Furthermore, ANN and AB demonstrated relatively weaker performance. Moreover, a reliability assessment conducted on both training and testing datasets also confirmed the results of the comparative analysis. Overall, the results enhance the assertion that ML models can sufficiently predict WQI, thereby enhancing water quality management.

关键词： water quality index rivers machine learning support vector machine extreme learning machine boosting algorithms

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An Empirical Study on the Performance of Cost-Sensitive boosting algorithms with Different Levels of Class Imbalance

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-12页

作者： Yin, Qing-Yan Zhang, Jiang-She Zhang, Chun-Xia Liu, Sheng-Cai Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

Cost-sensitive boosting algorithms have proven successful for solving the difficult class imbalance problems. However, the influence of misclassification costs and imbalance level on the algorithm performance is still not clear. The present paper aims to conduct an empirical comparison of six representative cost-sensitive boosting algorithms, including AdaCost, CSB1, CSB2, AdaC1, AdaC2, and AdaC3. These algorithms are thoroughly evaluated by a comprehensive suite of experiments, in which nearly fifty thousands classification models are trained on 17 real-world imbalanced data sets. Experimental results show that AdaC serial algorithms generally outperform AdaCost and CSB when dealing with different imbalance level data sets. Furthermore, the optimality of AdaC2 algorithm stands out around the misclassification costs setting: C-N = 0.7, C-p = 1, especially for dealing with strongly imbalanced data sets. In the case of data sets with a low-level imbalance, there is no significant difference between the AdaC serial algorithms. In addition, the results indicate that AdaC1 is comparatively insensitive to the misclassification costs, which is consistent with the finding of the preceding research work.

关键词： boosting algorithms EMPIRICAL research PROBLEM solving PERFORMANCE evaluation DATA analysis SET theory

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Recognition of human activities for wellness management using a smartphone and a smartwatch: A boosting approach

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DECISION SUPPORT SYSTEMS 2021年 140卷 113426-113426页

作者： Tarafdar, Pratik Bose, Indranil OP Jindal Global Univ Jindal Global Business Sch Informat Syst & Analyt Sonipat 131001 Haryana India Indian Inst Management Calcutta Diamond Harbour Rd Kolkata 700104 India

Mobile health applications are considered to be powerful tools for activity-based wellness management. With the availability of multimodal sensors in smart devices used in our daily lives, it is possible to track human activity and deliver context-aware wellness services. The embedded sensors in naturally used devices such as smartphones, smartwatches, and wearables contain rich information that can be integrated for human activity recognition. Our research demonstrates how powerful boosting algorithms can extract knowledge for human activity classification in a real-life setting. Our results show that boosting classifiers outperform traditional machine learning classifiers in the detection of basic human activities such as walking, standing, sitting, exercise, and sleeping. Further, we perform feature engineering to compare the potential of a smartphone and a smartwatch in activity detection. Our feature engineering strategy provides directions about the selection of sensor features for improvement in classification of basic human activities. The theoretical and practical implications of this research for activity-based wellness management are also discussed.

关键词： Activity-based wellness management boosting algorithms Human activity recognition Machine learning Mobile health Multimodal sensors

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Smart Robust Feature Selection (SoFt) for imbalanced and heterogeneous data

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KNOWLEDGE-BASED SYSTEMS 2022年第0期236卷 107197-107197页

作者： Kasim, Henry King, Stephen Lee, Gary Kee Khoon Sirigina, Rajendra Prasad How, Shannon Shi Qi Hung, Terence Gih Guang Cent Technol & Strategy Grp Future Intelligence Technol Rolls Royce Singapore Singapore Nanyang Technol Univ Rolls RoyceNTU Corp Lab Singapore Singapore Cranfield Univ Transport & Mfg IVHM Ctr Sch Aerosp Bedford MK43 0AL England

Designing a smart and robust predictive model that can deal with imbalanced data and a heterogeneous set of features is paramount to its widespread adoption by practitioners. By smart, we mean the model is either parameter-free or works well with default parameters, avoiding the challenge of parameter tuning. Furthermore, a robust model should consistently achieve high accuracy regardless of any dataset (imbalance, heterogeneous set of features) or domain (such as medical, financial). To this end, a computationally inexpensive and yet robust predictive model named smart robust feature selection (SoFt) is proposed. SoFt involves selecting a learning algorithm and designing a filtering-based feature selection algorithm named multi evaluation criteria and Pareto (MECP). Two state-of-the-art gradient boosting methods (GBMs), CatBoost and H2O GBM, are considered potential candidates for learning algorithms. CatBoost is selected over H2O GBM due to its robustness with both default and tuned parameters. The MECP uses multiple parameter-free feature scores to rank the features. SoFt is validated against CatBoost with a full feature set and wrapper-based CatBoost. SoFt is robust and consistent for imbalanced datasets, i.e., average value and standard deviation of log loss are low across different folds of K-fold cross-validation. Features selected by MECP are also consistent, i.e., features selected by SoFt and wrapper-based CatBoost are consistent across different folds, demonstrating the effectiveness of MECP. For balanced datasets, MECP selects too few features, and hence, the log loss of SoFt is significantly higher than CatBoost with a full feature set. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Class-imbalanced data Heterogeneous features boosting algorithms Feature Selection CatBoost H2O GBM

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An iterative boosting-based ensemble for streaming data classification

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INFORMATION FUSION 2019年 45卷 66-78页

作者： Bertini Junior, Joao Roberto Nicoletti, Maria do Carmo Univ Estadual Campinas Sch Technol Rua Paschoal Marmo 1888 Limeira Brazil FACCAMP Campo Limpo Paulista SP Brazil Univ Fed Sao Carlos Comp Sci Dept Sao Carlos SP Brazil

Among the many issues related to data stream applications, those involved in predictive tasks such as classification and regression, play a significant role in Machine Learning (ML). The so-called ensemble-based approaches have characteristics that can be appealing to data stream applications, such as easy updating and high flexibility. In spite of that, some of the current approaches consider unsuitable ways of updating the ensemble along with the continuous stream processing, such as growing it indefinitely or deleting all its base learners when trying to overcome a concept drift. Such inadequate actions interfere with two inherent characteristics of data streams namely, its possible infinite length and its need for prompt responses. In this paper, a new ensemblebased algorithm, suitable for classification tasks, is proposed. It relies on applying boosting to new batches of data aiming at maintaining the ensemble by adding a certain number of base learners, which is established as a function of the current ensemble accuracy rate. The updating mechanism enhances the model flexibility, allowing the ensemble to gather knowledge fast to quickly overcome high error rates, due to concept drift, while maintaining satisfactory results by slowing down the updating rate in stable concepts. Results comparing the proposed ensemble-based algorithm against eight other ensembles found in the literature show that the proposed algorithm is very competitive when dealing with data stream classification.

关键词： Ensemble learning Data stream classification boosting algorithms Incremental learning

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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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International agricultural trade forecasting using machine learning

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DATA & POLICY 2021年第1期3卷 e1-e1页

作者： Gopinath, Munisamy Batarseh, Feras A. Beckman, Jayson Kulkarni, Ajay Jeong, Sei Univ Georgia Dept Agr & Appl Econ Athens GA 30602 USA Virginia Polytech Inst & State Univ Bradley Dept Elect & Comp Engn Virginia Tech Arlington VA 24061 USA USDA Econ Res Serv Washington DC USA George Mason Univ Coll Sci Fairfax VA 22030 USA

Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques arc trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which arc often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

关键词： agriculture boosting algorithms forecasting machine learning trade flows

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Customer Transaction Prediction System

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Procedia Computer Science 2020年 168卷 49-56页

作者： Devendra Prakash Jaiswal Srishti Kumar Partha Mukherjee Pennsylvania State University 30 E. Swedesford Road Great Valley Malvern 19355 USA

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

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SGB-ELM: An Advanced Stochastic Gradient boosting-Based Ensemble Scheme for Extreme Learning Machine

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018年第1期2018卷 1-14页

作者： Guo, Hua Wang, Jikui Ao, Wei He, Yulin Lanzhou Univ Finance & Econ Sch Informat Engn Lanzhou 730020 Gansu Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

A novel ensemble scheme for extreme learning machine (ELM), named Stochastic Gradient boosting-based Extreme Learning Machine (SGB-ELM), is proposed in this paper. Instead of incorporating the stochastic gradient boosting method into ELM ensemble procedure primitively, SGB-ELM constructs a sequence of weak ELMs where each individual ELM is trained additively by optimizing the regularized objective. Specifically, we design an objective function based on the boosting mechanism where a regularization item is introduced simultaneously to alleviate overfitting. Then the derivation formula aimed at solving the output-layer weights of each weak ELM is determined using the second-order optimization. As the derivation formula is hard to be analytically calculated and the regularized objective tends to employ simple functions, we take the output-layer weights learned by the current pseudo residuals as an initial heuristic item and thus obtain the optimal output-layer weights by using the derivation formula to update the heuristic item iteratively. In comparison with several typical ELM ensemble methods, SGB-ELM achieves better generalization performance and predicted robustness, which demonstrates the feasibility and effectiveness of SGB-ELM.

关键词： MACHINE learning STOCHASTIC models boosting algorithms STATISTICAL ensembles MATHEMATICAL optimization

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

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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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