The study of foreign exchange trading revenue model using decision tree and gradient boosting

Jung, Ji Hyeon; Min, Dae Kee

doi:10.7465/jkdi.2013.24.1.161

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…The GB model has no randomness and builds trees, the depth of which does not exceed five per tree. Therefore, the GB modeling method can be said to connect as many shallow trees as possible [26]. Friedman's (2001) GB algorithm is as follows [5,27].…”

Section: Overview Of Gradient Boosting and Research Casesmentioning

confidence: 99%

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Urban River Dissolved Oxygen Prediction Model Using Machine Learning

Moon

Lee

et al. 2022

Water

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This study outlines the preliminary stages of the development of an algorithm to predict the optimal WQ of the Hwanggujicheon Stream. In the first stages, we used the AdaBoost algorithm model to predict the state of WQ, using data from the open artificial intelligence (AI) hub. The AdaBoost algorithm has excellent predictive performance and model suitability and was selected for random forest and gradient boosting (GB)-based boosting models. To predict the optimized WQ, we selected pH, SS, water temperature, total nitrogen(TN), dissolved total phosphorus(DTP), NH₃-N, chemical oxygen demand (COD), dissolved total nitrogen (DTN), and NO₃-N as the input variables of the AdaBoost model. Dissolved oxygen (DO) was used as the target variable. Third, an algorithm showing excellent predictive power was selected by analyzing the prediction accuracy according to the input variable by using the random forest or GB series algorithm in the initial model. Finally, the performance evaluation of the ultimately developed predictive model demonstrated that RMS was 0.015, MAE was 0.009, and R2 was 0.912. The coefficient of the variation of the root mean square error (CVRMSE) was 17.404. R2 0.912 and CVRMSE were 17.404, indicating that the predictive model developed meets the criteria of ASHRAE Guideline 14. It is imperative that government and administrative agencies have access to effective tools to assess WQ and pollution levels in their local bodies of water.

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Section: Overview Of Gradient Boosting and Research Casesmentioning

confidence: 99%

“…. , C m are combined into one classifier by the classifier C k with weight log(γ m ) to create a final classifier [26].…”

Section: Overview Of Gradient Boosting and Research Casesmentioning

confidence: 99%

Urban River Dissolved Oxygen Prediction Model Using Machine Learning

Moon

Lee

et al. 2022

Water

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“…Boosting is an iterative procedure used to change the distribution of data so that the basic classifier focuses on hard-to-classify data. Among them, gradient boosting (GBM) forms a final classifier by repeating the process of giving a high weight for the incorrectly classified observation value, but a low weight for the correctly classified observation value to the next observation by starting with the same weight for the observed data [27]. It approximates the function f, which is as shown in Equation ( 4), based on a linear combination of weak learners h. It is optimized in a greedy manner by selecting parameter θ j and weight α j of a weak learner iteratively [28].…”

Section: Gbm (Gradient Boosting Algorithm)mentioning

confidence: 99%

Development of a Safety Management System Tracking the Weight of Heavy Objects Carried by Construction Workers Using FSR Sensors

Lee

Son

2021

Applied Sciences

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It has been pointed out that the act of carrying a heavy object that exceeds a certain weight by a worker at a construction site is a major factor that puts physical burden on the worker’s musculoskeletal system. However, due to the nature of the construction site, where there are a large number of workers simultaneously working in an irregular space, it is difficult to figure out the weight of the object carried by the worker in real time or keep track of the worker who carries the excess weight. This paper proposes a prototype system to track the weight of heavy objects carried by construction workers by developing smart safety shoes with FSR (Force Sensitive Resistor) sensors. The system consists of smart safety shoes with sensors attached, a mobile device for collecting initial sensing data, and a web-based server computer for storing, preprocessing and analyzing such data. The effectiveness and accuracy of the weight tracking system was verified through the experiments where a weight was lifted by each experimenter from +0 kg to +20 kg in 5 kg increments. The results of the experiment were analyzed by a newly developed machine learning based model, which adopts effective classification algorithms such as decision tree, random forest, gradient boosting algorithm (GBM), and light GBM. The average accuracy classifying the weight by each classification algorithm showed similar, but high accuracy in the following order: random forest (90.9%), light GBM (90.5%), decision tree (90.3%), and GBM (89%). Overall, the proposed weight tracking system has a significant 90.2% average accuracy in classifying how much weight each experimenter carries.

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