eXtreme Gradient Boosting for Identifying Individual Users Across Different Digital Devices

Song, Rongwei; Chen, Siding; Deng, Bailong; Li, Li

doi:10.1007/978-3-319-39937-9_4

Cited by 24 publications

(13 citation statements)

References 12 publications

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“…ease of interpretation and invariant to input scale, and much easier to tune). Both of these methods are widely used as they outperform other distance-based algorithms like logistic regression, support vector machine, kNN in data science [4,14,[18][19][20][21].…”

Section: Machine Learning Algorithms For Imbalanced Datamentioning

confidence: 99%

Machine Learning Predictions for Underestimation of Job Runtime on HPC System

Guo

Nomura

Barton

et al. 2018

Lecture Notes in Computer Science

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In modern high-performance computing (HPC) systems, users are usually requested to estimate the job runtime for system scheduling when they submit a job. In general, an underestimation of job runtime will cause the HPC system to terminate the job before its completion. If users could be notified that their jobs may not finish before its allocated time expires, users can take actions, such as killing the job and resubmitting it after parameter adjustment, to save time and cost. Meanwhile, the productivity of HPC systems could also be vastly improved. In this paper, we propose a data-driven approach -that is, one that actively observes, analyzes, and logs jobs -for predicting underestimation of job runtime on HPC systems. Using data produced by TSUBAME 2.5, a supercomputer deployed at the Tokyo Institute of Technology, we apply machine learning algorithms to recognize patterns about whether the underestimation of job runtime occurs. Our experimental results show that our approach on runtime-underestimation prediction with 80% precision, 70% recall and 74% F1-score on the entirety of a given dataset. Finally, we split the entire job data set into subsets categorized by scientific application name. The best precision, recall and F1-score of subsets on runtime-underestimation prediction achieved 90%, 95% and 92% respectively.

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Section: Machine Learning Algorithms For Imbalanced Datamentioning

confidence: 99%

Machine Learning Predictions for Underestimation of Job Runtime on HPC System

Guo

Nomura

Barton

et al. 2018

Lecture Notes in Computer Science

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“…In the context of the specialization-diversification debate, the present results indicate that from today's perspective there is a debate about a "production function," the structure of which is still unknown to date. Even by implementing one of the seemingly most promising procedures in supervised machine learning (Song et al, 2016), we have not (yet) been able to discover a model with an acceptable detection rate.…”

Section: Resultsmentioning

confidence: 99%

A Machine Learning Approach to “Revisit” Specialization and Sampling in Institutionalized Practice

2019

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The question which nature and scope of developmental participation patterns lead to international senior-level success has been controversially discussed in the literature for many years. The present article aimed to extend existing literature in two respects. First, we reviewed studies comparing developmental sport activities of international-level and national-level athletes. The results indicated that comparisons among the highest success levels are infrequent, findings partly varied across studies, while the practice volume in other sports, but not in the athlete's main sport, mostly distinguished international-level from national-level athletes. Second, a new methodical approach combining decision trees and gradient boosting (conducted under the R environment) was applied to data from a previously published study. It allowed for multivariate, interactive, and nonlinear analysis and was promising to achieve relatively better explanation than earlier, traditional procedures. The results indicate that some formerly found differences between international and national-level athletes in the volume of main-sport and other-sports practice may represent artifacts of uncontrolled age effects, rather than variables factually differentiating success. In the context of the specialization-diversification debate, the present findings suggest that the debate addresses a "production function," the structure of which is still unknown. Practice-related recommendations on developmental participation patterns are apparently expressions of highly rationalized myths, rather than evidence-based efficient norms.

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“…The prediction results of the XGBOOST models were better than those made by the BPNN model with the same sample requirements. The XGBOOST model has many advantages, including a simple training process, low computer-processing costs, and fast convergence, compared to ANNs [47]. Therefore, using the XGBOOST model is very advantageous for predicting TC intensity.…”

Section: Discussionmentioning

confidence: 99%

Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

et al. 2019

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Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015, and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.The characteristics that affect TC intensity are nonlinear and thus difficult to predict [2]. In recent years, many researchers have studied TC intensity prediction primarily using numerical forecasting and statistical methods [6][7][8][9][10]. Numerical forecasting is the main tool used to forecast TCs around the world, and systems such as the European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System (ECMWF-IFS) [11], the Japan Meteorological Agency's global spectral model (JMA-GSM) [12], and the National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) [13] have been developed as operational techniques. Statistical intensity forecast methods such as Climatology and Persistence (CLIPER) [14] and the Statistical Hurricane Intensity Prediction Scheme (SHIPS) [15] have also been developed as operational techniques. However, advancements in TC intensity prediction have been relatively slow, although notable work has been done in the past 20 years on predicting TC paths [16]. One of the reasons for this is that the internal structure of storms is not yet sufficiently understood. TCs have an asymmetric structure, which may be caused by thermal and dynamic factors such as uneven distribution of sea surface temperature and humidity, horizontal or vertical shear, and asymmetric distribution of convection [2,17]. Changes in TC intensity are controlled by many e...

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eXtreme Gradient Boosting for Identifying Individual Users Across Different Digital Devices

Cited by 24 publications

References 12 publications

Machine Learning Predictions for Underestimation of Job Runtime on HPC System

Machine Learning Predictions for Underestimation of Job Runtime on HPC System

A Machine Learning Approach to “Revisit” Specialization and Sampling in Institutionalized Practice

Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

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