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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Estimation And Interpretation Of Machine Learning Models With Customized Surrogate Model, Mudabbir Ali, Asad Masood Khattak, Zain Ali, Bashir Hayat, Muhammad Idrees, Zeeshan Pervez, Kashif Rizwan, Tae Eung Sung, Ki Il Kim
Estimation And Interpretation Of Machine Learning Models With Customized Surrogate Model, Mudabbir Ali, Asad Masood Khattak, Zain Ali, Bashir Hayat, Muhammad Idrees, Zeeshan Pervez, Kashif Rizwan, Tae Eung Sung, Ki Il Kim
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Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are …
Hybrid Feature Selection Approach To Identify Optimal Features Of Profile Metadata To Detect Social Bots In Twitter, Eiman Alothali, Kadhim Hayawi, Hany Alashwal
Hybrid Feature Selection Approach To Identify Optimal Features Of Profile Metadata To Detect Social Bots In Twitter, Eiman Alothali, Kadhim Hayawi, Hany Alashwal
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The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector …