Multi-view Ensemble Learning Using Rough Set Based Feature Ranking for Opinion Spam Detection

Saini, Mayank; Verma, Sharad; Sharan, Aditi

doi:10.1007/978-981-13-0341-8_1

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…Saini M et al [31] extracted different textual characteristics from text reviews and used XGBoost to build the classifier. In Xu et al's study, the feature extraction was performed on existing normal and malicious requests, and XGBoost classification algorithm was adopted to identify abnormal requests.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the idea of classification, the researchers have designed numerical form characteristics to identify spam users. The supervised machine learning algorithm can be split into a single classification algorithm and an integrated classification algorithm (e.g., Support Vector Machine (SVM) [3] [8][9][10][11] [13][14], meta-classifiers (Decorate, Logit Boost) [4], Naive Bayesian (NB) [6][9] [11], Back Propagation Neural Network (BP) [16], Radial Basis Function (RBF) [18], Extreme Learning Machine (ELM) [8] [22], K-nearest Neighbor (KNN) [9] [19], Decision Tree (DT) [9] [20], Random Forest (RF) [5] [7][8][9][ [23][24][25][26] and eXtreme Gradient Boosting (XGBoost) [31,32]).…”

Section: Introductionmentioning

confidence: 99%

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Detection of Social Network Spam Based on Improved Extreme Learning Machine

2020

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With the rapid advancement of the online social network, social media like Twitter has been increasingly critical to real life and become the prime objective of spammers. Twitter spam detection refers to a complex task for the involvement of a range of characteristics, and spam and non-spam have caused unbalanced data distribution in Twitter. To solve the mentioned problems, Twitter spam characteristics are analyzed as the user attribute, content, activity and relationship in this study, and a novel spam detection algorithm is designed based on regularized extreme learning machine, called the Improved Incremental Fuzzy-kernel-regularized Extreme Learning Machine (I2FELM), which is used to detect the Twitter spam accurately. As revealed from the experience validation results, the proposed I2FELM can efficiently identify the balanced and unbalanced dataset. Moreover, with few characteristics taken, the I2FELM can more effectively detect spam, which proves the effectiveness of the algorithm.

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Section: Related Workmentioning

confidence: 99%