.png){kind=icon}
返回
Predicting online news popularity based on machine learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2022.108198.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to its fast transmission and easy accessibility features, the Internet has replaced traditional newspapers and magazines as the main channel for delivering public news. Hence, predicting the popularity of Internet news has become an essential topic. This research is based on a UCI dataset, the primary source of which is Mashable News, one of the major blogs in the world. The number of shared articles is used as a predictor of the popularity of the news, and the four types of machine learning algorithms utilized are Random Forest, LightGBM, XGBoost, and One-Class SVM. The best prediction method is One-Class SVM with 88% accuracy. This result indicates that combining Autoencoder and One-Class algorithm will optimize the prediction while detecting anomalies within imbalanced data.
Keyword:
Machine learning
Internet news
Prediction
Autoencoder
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
UTILITY OF THROMBOELASTOGRAPHY VERSUS ROUTINE COAGULATION TESTS: CARDIAC BYPASS SURGERY血栓弹力图与常规凝血试验的应用价值：心脏 bypass 手术
PREAI
分享收藏
分享收藏A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容