Browsing by Author "Mojeed, Hammed Adeleye"
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Item Heterogeneous ensemble with combined dimensionality reduction for social spam detection(International Association of Online Engineering, 2021) Oladepo, Abdulfatai Ganiyu; Bajeh, Amos Orenyi; Balogun, Abdullateef Oluwagbemiga; Mojeed, Hammed Adeleye; Salman, Abdulsalam Abiodun; Bako, Abdullateef IyandaSpamming is one of the challenging problems within social networks which involves spreading malicious or scam content on a network; this often leads to a huge loss in the value of real-time social network services, compromise the user and system reputation and jeopardize users trust in the system. Existing methods in spam detection still suffer from misclassification caused by redundant and irrelevant features in the dataset as a result of high dimensionality. 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. To empirically investigate its performance, 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 (Ensemble + IG + PCA)outperformed other experimented methods on studied spam datasets. Specifically, the proposed framework 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 framework had better performance than some existing approaches. 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 model for detecting spam contents.Item Parameter tuning in KNN for software defect prediction: an empirical analysis(Department of Computer Engineering, Universitas Diponegoro, Indonesia., 2019-10-31) Mabayoje, Modinat Abolore; Balogun, Abdullateef Oluwagbemiga; Jibril, Hajarah Afor; Atoyebi, Jelili Olaniyi; Mojeed, Hammed Adeleye; Adeyemo, Victor ElijahSoftware Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.Item Wrapper Feature Selection based Heterogeneous Classifiers for Software Defect Prediction(Adeleke University, Ede., 2019-02) Mabayoje, Modinat Abolore; Balogun, Abdullateef Oluwagbemiga; Bello, Shade Musllimat; Atoyebi, Jelili Olaniyi; Mojeed, Hammed Adeleye; Ekundayo, Ayobami HalimatThe performance of Software Defect Prediction (SDP) models depends on the quality of dataset used for training the models. The high dimensionality of software metric features has been noted as a data quality problem which affects the performance of SDP models. This makes it crucial to apply feature selection (FS) to SDP since FS can remove irrelevant and redundant software metric features. In this study, the effect of wrapper-based FS methods on classification techniques in SDP was investigated. The wrapper FS methods were based on different search methods; Best First Search (BFS), Genetic Search (GS), Greedy Stepwise Search (GSS) and Multi-Objective Evolutionary Search (MOES) so as to investigate their respective effect on classifiers in SDP. Five (5) publicly available software defect datasets were used. These datasets were classified by the individual classifiers which were carefully selected based on their characteristics hence the heterogeneity. Naïve Bayes (NB) was selected from Bayes category Classifier, K-Nearest Neighbor (KNN) was selected from Instance-Based Learner category and (J48) Decision Tree from Trees Function classifier. The experimental results clearly showed that the application of wrapper FS method to datasets before classification in SDP is better and should be encouraged as NB with GS based Wrapper Method had the best accuracy performance. It can be concluded that FS methods are capable of improving the performance of predictive models in SDP.