Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

High dimensionality

Ensemble

Spam detection

This study presents a novel framework based on a heterogeneous ensemble method and a hybrid dimensionality reduction technique for spam detection in micro-blogging social networks. A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR) and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers based on Average of Probabilities (AOP) was used for spam detection. The proposed framework was applied on MPI_SWS and SAC’13 Tip spam datasets and the developed models were evaluated based on accuracy, precision, recall, f-measure, and area under the curve (AUC). From the experimental results, the proposed framework (that is, Ensemble + IG + PCA) outperformed other experimented methods on studied spam datasets. Specifically, the proposed method had an average accuracy value of 87.5%, an average precision score of 0.877, an average recall value of 0.845, an average F-measure value of 0.872 and an average AUC value of 0.943. Also, the proposed method had better performance than some existing methods. Consequently, this study has shown that addressing high dimensionality in spam datasets, in this case, a hybrid of IG and PCA with a heterogeneous ensemble method can produce a more effective method for detecting spam contents.

Oladepo, Abdulfatai Ganiyu

Bajeh, Amos Orenyi

Balogun, Abdullateef Oluwagbemiga

Mojeed, Hammed Adeleye

Salman, Abdulsalam Abiodun

Bako, Abdullateef Iyanda

International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 17 (2021); pp. 84-103

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2021-09-06

Copyright (c) 2021 Abdulfatai Oladepo, Amos Bajeh, Abdullateef Balogun, Hammed Mojeed, Salman Abiodun, Abdullateef Bako

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Peer-reviewed Article