scholarly journals HitBoost: Survival Analysis via a Multi-Output Gradient Boosting Decision Tree Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 56785-56795
Author(s):  
Pei Liu ◽  
Bo Fu ◽  
Simon X. Yang
Keyword(s):  
Survival Analysis ◽  
Decision Tree ◽  
Gradient Boosting ◽  
Decision Tree Method ◽  
Tree Method

scholarly journals Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

2018 ◽  
Vol 10 (2) ◽  
pp. 185 ◽  
Author(s):  
Lu Yang ◽  
Xiaotong Zhang ◽  
Shunlin Liang ◽  
Yunjun Yao ◽  
Kun Jia ◽  
...  
Keyword(s):  
Decision Tree ◽  
Shortwave Radiation ◽  
Gradient Boosting ◽  
Downward Shortwave Radiation ◽  
Decision Tree Method ◽  
Tree Method

Estimation of Gourami Supplies Using Gradient Boosting Decision Tree Method of XGBoost

TEM Journal ◽  
2021 ◽  
pp. 144-151
Author(s):  
I Made Sukarsa ◽  
Ngakan Nyoman Pandika Pinata ◽  
Ni Kadek Dwi Rusjayanthi ◽  
Ni Wayan Wisswani
Keyword(s):  
Decision Tree ◽  
Absolute Error ◽  
Feature Model ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Transaction Data ◽  
Time Period ◽  
Decision Tree Method ◽  
The Right ◽  
Tree Method

The need for food supplies are very crucial in a food business, therefore it is necessary to estimate the right supplies to maximize profit. One of the methods to determine these is by looking for patterns and forecasting transaction data. The purpose of this research is to estimate the gourami supplies using transaction data to forecast using the gradient boosting decision tree method from XGBoost. The transaction data used comes from Restaurant X with a time period from 2016 to 2019. A measurement error rate of the model is achieved by using MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This study tried five XGBoost models with different features such as lag, rolling window, mean encoding, and mix. The results of this study indicate that the mixed feature model produces an accuracy of 97.54% with an MAE of 0.63 and a MAPE of 2.64%.

Rancang Bangun Aplikasi Sistem Pakar untuk Menangani Masalah Kecantikan pada Wajah Menggunakan Metode Decision Tree

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun
Keyword(s):  
Decision Tree ◽  
Uneven Distribution ◽  
Dry Skin ◽  
Average Accuracy ◽  
Accuracy Level ◽  
Index Terms ◽  
Decision Tree Method ◽  
Tree Method

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.

Marketing Model Investigation of Cable Television Service Based on BP Neural Network and Decision Tree

2013 ◽  
Vol 774-776 ◽  
pp. 1757-1761
Author(s):  
Bing Xiang Liu ◽  
Xu Dong Wu ◽  
Ying Xi Li ◽  
Xie Wei Wang
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Bp Neural Network ◽  
Cable Television ◽  
Model Building ◽  
Television System ◽  
Marketing Model ◽  
Television Service ◽  
Decision Tree Method ◽  
Tree Method

This paper takes more than four hundred records of some cable television system for example, makes data mining according to users data record, uses BP neural network and decision tree method respectively to have model building and finds the best model fits for users to order press service. The results of the experiment validate the methods feasibility and validity.

Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree

2011 ◽  
Vol 403-408 ◽  
pp. 1804-1807
Author(s):  
Ning Zhao ◽  
Shao Hua Dong ◽  
Qing Tian
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Arc Welding ◽  
Data Cleaning ◽  
Data Extraction ◽  
Electric Arc ◽  
Welded Tube ◽  
Electric Arc Welding ◽  
Decision Tree Method ◽  
Tree Method

In order to optimize electric- arc welding （ERW） welded tube scheduling , the paper introduces data cleaning, data extraction and transformation in detail and defines the datasets of sample attribute, which is based on analysis of production process of ERW welded tube. Furthermore, Decision-Tree method is adopted to achieve data mining and summarize scheduling rules which are validated by an example.

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