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Variance analysis of software ageing problems

IET SOFTWARE 2018年第1期12卷 41-48页

作者： Yan, Yongquan Shanxi Univ Finance & Econ Fac Stat Taiyuan Shanxi Peoples R China

Software ageing problems are mainly caused by resource consumption exhaustion, so many researchers focused on predicting software resource consumption. However, the loss analysis using variance has not been done. In this study, the authors propose a framework to analyse variance change in the resource consumption prediction problems. This framework is made up of three steps. First, an original variance decomposition is proposed in view of data sampling and partitioning process. Second, in order to study the influence of data sampling and partitioning process to the variance, the enhanced Friedman test plus Nemenyi post-hoc test is introduced. Lastly, they propose a corrected t-test to analyse the performances of two regression algorithms: auto-regressive integrated moving average and artificial neuron network. In the experiments, they analyse the variance in two levels: operating system level and application level. They find the result that k is equal to ten for k-fold cross-validation is proper for resource consumption prediction, although the contribution to variance is almost same for the sensitivity of forecasted estimation loss in consideration of data partitioning process and the sensitivity of forecasted estimation loss in consideration of data sampling procedure.

关键词： software metrics data handling regression analysis neural nets autoregressive moving average processes variance change analysis software ageing problems resource consumption prediction problems variance decomposition data partitioning process regression algorithms autoregressive integrated moving average artificial neuron network forecasted estimation loss data sampling procedure

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A Comparison Framework of Machine Learning algorithms for Mixed-Type Variables Datasets: A Case Study on Tire-Performances Prediction

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IEEE ACCESS 2020年 8卷 214902-214914页

作者： Gutierrez-Gomez, Leonardo Petry, Frank Khadraoui, Djamel Luxembourg Inst Sci & Technol LIST L-4362 Esch Sur Alzette Luxembourg Goodyear Innovat Ctr Luxembourg L-7750 Colmar Berg Luxembourg

Many engineering applications in the automotive, aeronautic, rubber, mechanics, and manufacturing industries collect multiple datasets measuring physical relations between input variables and performances for modeling purposes. The challenge relies on that such data is often highly dimensional, non-linear and contain mixed variables, i.e., numerical and categorical features, requiring specific algorithms and encoding schemes to perform regression task efficiently. Moreover, defining an appropriated similarity criterion for mixed-type data is a non-trivial task, especially when it is meant to be used in regression problems. This paper discusses the use of different machine learning algorithms for regression problems, involving mixed-type variables across multiple datasets. We use tire-related datasets as a case study to perform a rigorous, statistically founded comparison of different machine learning algorithms with encoding schemes to handle mixed variables in the prediction of tire-performances across multiple tire-related datasets. Friedman's statistic and Nemenyi post-hoc tests are used to test the significance of performance differences between techniques and encoding strategies. Our contributions come as a series of recommendations for handling efficiently mixed-type variables while achieving high performances on regression tasks over multiple datasets. Furthermore, we provide a flexible and efficient similarity function between tires useful for tire comparison, prediction, and retrieval tasks.

关键词： Tires Encoding Task analysis Prediction algorithms Force Steel Support vector machines Mixed-type variables categorical encoding Friedman Nemenyi regression algorithms

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Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia

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WATER SUPPLY 2021年第5期21卷 1878-1894页

作者： Kapalanga, Taimi S. Hoko, Zvikomborero Gumindoga, Webster Chikwiramakomo, Loyd Univ Zimbabwe Dept Civil Engn Box MP 167 Mt Pleast Harare Zimbabwe

Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression-analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R-2 = 0.767);TN (R-2 = 0.798,);TP (R-2 = 0.907);TSS (R-2 = 0.284,) and total algae count (R-2 = 0.851). Prediction algorithms are generally the best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality.

关键词： Landsat 8 reflectance values regression algorithms remote sensing retrieval water quality

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Optimal selection of benchmarking datasets for unbiased machine learning algorithm evaluation

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DATA MINING AND KNOWLEDGE DISCOVERY 2024年第2期38卷 461-500页

作者： Pereira, Joao Luiz Junho Smith-Miles, Kate Munoz, Mario Andres Lorena, Ana Carolina Inst Tecnol Aeronaut Sao Jose Dos Campos Brazil Univ Melbourne Sch Math & Stat Melbourne Australia Univ Melbourne Sch Comp & Informat Syst Melbourne Australia

Whenever a new supervised machine learning (ML) algorithm or solution is developed, it is imperative to evaluate the predictive performance it attains for diverse datasets. This is done in order to stress test the strengths and weaknesses of the novel algorithms and provide evidence for situations in which they are most useful. A common practice is to gather some datasets from public benchmark repositories for such an evaluation. But little or no specific criteria are used in the selection of these datasets, which is often ad-hoc. In this paper, the importance of gathering a diverse benchmark of datasets in order to properly evaluate ML models and really understand their capabilities is investigated. Leveraging from meta-learning studies evaluating the diversity of public repositories of datasets, this paper introduces an optimization method to choose varied classification and regression datasets from a pool of candidate datasets. The method is based on maximum coverage, circular packing, and the meta-heuristic Lichtenberg Algorithm for ensuring that diverse datasets able to challenge the ML algorithms more broadly are chosen. The selections were compared experimentally with a random selection of datasets and with clustering by k-medoids and proved to be more effective regarding the diversity of the chosen benchmarks and the ability to challenge the ML algorithms at different levels.

关键词： Benchmark datasets' suites Instance space analysis Classification algorithms regression algorithms Meta-learning Optimization

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Rapid determination of moisture content of multi-source solid waste using ATR-FTIR and multiple machine learning methods

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WASTE MANAGEMENT 2022年 153卷 20-30页

作者： Qi, Ya-Ping He, Ping-Jing Lan, Dong-Ying Xian, Hao-Yang Lu, Fan Zhang, Hua Tongji Univ Inst Waste Treatment & Reclamat Coll Environm Sci & Engn Shanghai 200092 Peoples R China Shanghai Inst Pollut Control & Ecol Secur Shanghai 200092 Peoples R China Shanghai Engn Res Ctr Multisource Solid Wastes Cop Shanghai 200092 Peoples R China Tongji Univ Inst Waste Treatment & Reclamat Shanghai 200092 Peoples R China

Rapid determination of moisture content plays an important role in guiding the recycling, treatment and disposal of solid waste, as the moisture content of solid waste directly affects the leachate generation, microbial activities, pollutants leaching and energy consumption during thermal treatment. Traditional moisture content measurement methods are time-consuming, cumbersome and destructive to samples. Therefore, a rapid and nondestructive method for determining the moisture content of solid waste has become a key technology. In this work, an attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and multiple machine learning methods was developed to predict the moisture content of multi-source solid waste (textile, paper, leather and wood waste). A combined model was proposed for moisture content regression prediction, and the applicability of 20 combinations of five spectral preprocessing methods and four regression algorithms were discussed to further improve the modeling accuracy. Furthermore, the prediction result based on the water-band spectra was compared with the prediction result based on the full-band spectra. The result showed that the combination model can efficiently predict the moisture content of multi-source solid waste, and the R-2 values of the validation and test datasets and the root mean square error for the moisture prediction reached 0.9604, 0.9660, and 3.80, respectively after the hyperparameter optimization. The excellent performance indicated that the proposed combined models can rapidly and accurately measure the moisture content of solid waste, which is significant for the existing waste characterization scheme, and for the further real-time monitoring and management of solid waste treatment and disposal process.

关键词： Machine learning Solid waste Prediction Moisture content Spectra preprocessing regression algorithms

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Machine learning algorithms to forecast air quality: a survey

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第9期56卷 10031-10066页

作者： Mendez, Manuel Merayo, Mercedes G. Nunez, Manuel Univ Complutense Madrid Design & Testing Reliable Syst Res Grp C Prof Jose Garcia St esmases 9 Madrid 28040 Madrid Spain

Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011-2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.

关键词： Machine learning Deep learning regression algorithms Air quality

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Machine learning as a design aid for electrical machines

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ELEKTROTECHNIK UND INFORMATIONSTECHNIK 2023年第3-4期140卷 356-365页

作者： Hofmann, Wilfried Tech Univ Dresden Elektrotech Inst Lehrstuhl Elekt Maschinen & Antriebe Helmholtzstr 9 D-01069 Dresden Germany

Machine learning can help to reduce costly finite element calculations for finding optimal solutions for the design of electrical machines if one can draw on the results of numerous existing calculations and simulations. After systematizing the quantities associated with the design process, the results presented here provide a basis for selecting algorithms and preprocessing the data available. The desired target variables of a design as well as the boundary conditions are inputs for a learning algorithm to predict the sought unknowns. For this purpose, user libraries such as scikit-learn, which is used here, offer a large number of parameterizable algorithms. The simplified electrical equivalent circuit diagram of a three-phase induction machine can, for example, be used to identify differences both between individual parameters and between concrete regression algorithms. The training data for the investigations are taken from a programmed design tool, which forms a comprehensive modeling basis with over 120 electromagnetic design variables. For the creation of the training data, it is important that both "good" and "bad" design data sets are available for learning the algorithms.

关键词： Design Electrical machines Machine learning regression algorithms

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Membrane Reflector Surface Reconstruction Using Zernike Polynomials and Two-stage Algorithm

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IFAC-PapersOnLine 2015年第28期48卷 632-637页

作者： Chen, Zhenyu Liu, Yang Xu, Xiandong Li, Kang Chen, Xinglin Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast BelfastBT9 5AH United Kingdom

The membrane reflector is an important component in space optical systems. In a membrane reflector forming process, the surface reconstructed by discrete sampling points is used as a feedback signal for the forming control. Precise and efficient surface reconstruction directly affects the controller design and the control performance. As the most popular tool for optical surface reconstruction, the Zernike polynomials encounter the contradictory requirements between performance and model complexity. The reconstruction error may become no-convergent due to ill-conditioned matrix with highdegree polynomials. Therefore, the Zernike polynomial term selection becomes a key and hot topic. This paper proposes to construct the Zernike polynomials based model using a two-stage algorithm. The experimental results confirm the effectiveness of the proposed technique. © 2015

关键词： Polynomial regression Controllers Genetic algorithms Identification (control systems) Least squares approximations Numerical methods Polynomials Reflection Surface reconstruction Ill conditioned matrices Least Square Membrane reflectors Reconstruction error regression algorithms Space optical system Stability of numerical methods Two stage algorithm

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Quantifying performance of machine learning methods for neuroimaging data

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NEUROIMAGE 2019年 199卷 351-365页

作者： Jollans, Lee Boyle, Rory Artiges, Eric Banaschewski, Tobias Desrivieres, Sylvane Grigis, Antoine Martinot, Jean-Luc Paus, Tomas Smolka, Michael N. Walter, Henrik Schumann, Gunter Garavan, Hugh Whelan, Robert Trinity Coll Dublin Sch Psychol Dublin Ireland Max Planck Inst Psychiat Dept Translat Res Psychiat Munich Germany Univ Paris 05 Univ Paris Sud Sorbonne Paris Cite INSERM Unit Neuroimaging & Psychiat 1000Inst Nat Paris France Orsay Hosp Psychiat Dept 91G16 Orsay France Heidelberg Univ Med Fac Mannheim Cent Inst Mental Hlth Dept Child & Adolescent Psychiat & Psychotherapy Sq J5 D-68159 Mannheim Germany Kings Coll London Inst Psychiat Psychol & Neurosci Social Genet & Dev Psychiat Ctr MRC London England Univ Paris Saclay CEA NeuroSpin F-91191 Gif Sur Yvette France Maison de Solenn Paris France Univ Toronto Holland Bloorview Kids Rehabil Hosp Bloorview Res Inst Toronto ON M6A 2E1 Canada Univ Toronto Dept Psychol Toronto ON M6A 2E1 Canada Univ Toronto Dept Psychiat Toronto ON M6A 2E1 Canada Tech Univ Dresden Dept Psychiat Dresden Germany Tech Univ Dresden Neuroimaging Ctr Dresden Germany Charite Univ Med Berlin Charitepl 1 Berlin Germany Free Univ Berlin Charitepl 1 Berlin Germany Humboldt Univ Charitepl 1 Berlin Germany Berlin Inst Hlth Charitepl 1 Berlin Germany Dept Psychiat & Psychotherapy Charitepl 1 Berlin Germany Campus Charite Mitte Charitepl 1 Berlin Germany Univ Vermont Dept Psychiat Burlington VT USA Trinity Coll Dublin Global Brain Hlth Inst Dublin Ireland

Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques - embedded feature selection and bootstrap aggregation (bagging) - to model performance was also quantified. Five machine learning regression methods - Gaussian Process regression, Multiple Kernel Learning, Kernel Ridge regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge regression and Gaussian Process regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.

关键词： Machine learning Neuroimaging regression algorithms Reproducibility

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Brain-Machine Interfaces Lessons for Prosthetic Hand Control

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HAND CLINICS 2021年第3期37卷 391-399页

作者： Vaskov, Alex K. Chestek, Cynthia A. Univ Michigan Robot Inst 2505 Hayward St Ann Arbor MI 48109 USA Univ Michigan Dept Biomed Engn 2200 Bonisteel Blvd Ann Arbor MI 48109 USA Univ Michigan Dept Elect Engn & Comp Sci 1301 Beal Ave Ann Arbor MI 48109 USA Univ Michigan Neurosci Grad Program 204 Washtenaw Ave Ann Arbor MI 48109 USA

Brain-machine interfaces (BMI) are being developed to restore upper limb function for persons with spinal cord injury or other motor degenerative conditions. BMI and implantable sensors for myoelectric prostheses directly extract information from the central or peripheral nervous system to provide users with high fidelity control of their prosthetic device. Control algorithms have been highly transferable between the 2 technologies but also face common issues. In this review of the current state of the art in each field, the authors point out similarities and differences between the 2 technologies that may guide the implementation of common solutions to these challenges.

关键词： Brain-machine interfaces Fine motor control regression algorithms Pattern recognition Calibration methods

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