An Ensemble Learning Framework for Online Web Spam Detection

2013 ◽  
Author(s):  
Cailing Dong ◽  
Bin Zhou
Keyword(s):  
Ensemble Learning ◽  
Spam Detection ◽  
Learning Framework ◽  
Web Spam ◽  
Online Web

scholarly journals A Heterogeneous Ensemble Learning Framework for Spam Detection in Social Networks with Imbalanced Data

2020 ◽  
Vol 10 (3) ◽  
pp. 936 ◽  
Author(s):  
Chensu Zhao ◽  
Yang Xin ◽  
Xuefeng Li ◽  
Yixian Yang ◽  
Yuling Chen
Keyword(s):  
Social Networks ◽  
Ensemble Learning ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Real World Data ◽  
Learning Framework ◽  
The Impact ◽  
Base Module

The popularity of social networks provides people with many conveniences, but their rapid growth has also attracted many attackers. In recent years, the malicious behavior of social network spammers has seriously threatened the information security of ordinary users. To reduce this threat, many researchers have mined the behavior characteristics of spammers and have obtained good results by applying machine learning algorithms to identify spammers in social networks. However, most of these studies overlook class imbalance situations that exist in real world data. In this paper, we propose a heterogeneous stacking-based ensemble learning framework to ameliorate the impact of class imbalance on spam detection in social networks. The proposed framework consists of two main components, a base module and a combining module. In the base module, we adopt six different base classifiers and utilize this classifier diversity to construct new ensemble input members. In the combination module, we introduce cost sensitive learning into deep neural network training. By setting different costs for misclassification and dynamically adjusting the weights of the prediction results of the base classifiers, we can integrate the input members and aggregate the classification results. The experimental results show that our framework effectively improves the spam detection rate on imbalanced datasets.

Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates

Synlett ◽  
2020 ◽  
Author(s):  
Akira Yada ◽  
Kazuhiko Sato ◽  
Tarojiro Matsumura ◽  
Yasunobu Ando ◽  
Kenji Nagata ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Reaction Rates ◽  
Initial Reaction Rate ◽  
Training Dataset ◽  
Initial Reaction ◽  
Learning Approach ◽  
Learning Framework ◽  
Machine Learning Approach ◽  
Reasonable Prediction ◽  
Epoxidation Of Alkenes

AbstractThe prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset.

scholarly journals An Asymptotic Ensemble Learning Framework for Big Data Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 3675-3693 ◽  
Author(s):  
Salman Salloum ◽  
Joshua Zhexue Huang ◽  
Yulin He ◽  
Xiaojun Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Ensemble Learning ◽  
Big Data Analysis ◽  
Learning Framework

scholarly journals Comparative Study of Web Spam Detection using Data Mining

2013 ◽  
Vol 68 (18) ◽  
pp. 26-29
Author(s):  
Chirag Nathwani ◽  
Viralkumar Prajapati ◽  
Deven Agravat
Keyword(s):  
Data Mining ◽  
Comparative Study ◽  
Spam Detection ◽  
Web Spam ◽  
Using Data

scholarly journals Web Spam Detection by Learning from Small Labeled Samples

2012 ◽  
Vol 50 (21) ◽  
pp. 1-5 ◽  
Author(s):  
Jaber Karimpour ◽  
Ali A. Noroozi ◽  
Somayeh Alizadeh
Keyword(s):  
Spam Detection ◽  
Web Spam
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