Prediction of Ad Clicks Using Early Stop Based on XGBoost 


Vol. 46,  No. 6, pp. 993-1000, Jun.  2021
10.7840/kics.2021.46.6.993


PDF
  Abstract

Continuous data training on websites and social media platforms with machine learning algorithms that predict if a particular user clicks on ads results in better performance for training datasets, while test datasets experience overfitting problems that do not improve after a fixed number of learning iterations. In this paper, we propose an early stop of the learning process based on the XGBoost algorithm rather than the existing algorithm to avoid overfitting. XGBoost is a method to avoid overfitting by training complex data models, monitoring the performance of the models learned in a separate cluster of test data and stopping the training procedure if the performance of the test dataset has not improved after a fixed number of training iterations. We automatically select inflection points where the performance of the test dataset begins to decrease, thus implementing accuracy while avoiding overfitting, which continues to improve the performance of the training dataset according to the model"s overfitting. Finally, the experimental results showed performance improvements based on the XGBoot algorithm compared to the Logistic Regression algorithm and the Decision Tree algorithm.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

Y. Han and I. Joe, "Prediction of Ad Clicks Using Early Stop Based on XGBoost," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 6, pp. 993-1000, 2021. DOI: 10.7840/kics.2021.46.6.993.

[ACM Style]

Young-Jin Han and In-Whee Joe. 2021. Prediction of Ad Clicks Using Early Stop Based on XGBoost. The Journal of Korean Institute of Communications and Information Sciences, 46, 6, (2021), 993-1000. DOI: 10.7840/kics.2021.46.6.993.

[KICS Style]

Young-Jin Han and In-Whee Joe, "Prediction of Ad Clicks Using Early Stop Based on XGBoost," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 6, pp. 993-1000, 6. 2021. (https://doi.org/10.7840/kics.2021.46.6.993)

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880