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Behaviour Anomaly Detection With Similarity-Based Sampling for Imbalanced Data
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Author(s): Isha Y. Agarwal (Sardar Vallabhbhai National Institute of Technology, Surat, India), Dipti P. Rana (Sardar Vallabhbhai National Institute of Technology, Surat, India), Kshitij R. Suri (Sardar Vallabhbhai National Institute of Technology, Surat, India), Punitkumar Jain (Sardar Vallabhbhai National Institute of Technology, Surat, India), Saumya Awasthi (Sardar Vallabhbhai National Institute of Technology, Surat, India)and Krittika Roy (Sardar Vallabhbhai National Institute of Technology, Surat, India)
Copyright: 2021
Pages: 18
Source title:
Data Preprocessing, Active Learning, and Cost Perceptive Approaches for Resolving Data Imbalance
Source Author(s)/Editor(s): Dipti P. Rana (Sardar Vallabhbhai National Institute of Technology, Surat, India)and Rupa G. Mehta (Sardar Vallabhbhai National Institute of Technology, Surat, India)
DOI: 10.4018/978-1-7998-7371-6.ch010
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Abstract
Mental health is a major issue in our society, and people treat this issue as a subject that should not be spoken about. So, many such individuals utilize social media as a platform to share their thoughts and fears. This emphasizes the researchers to identify sufferers who require treatment. Many approaches have been devised to detect early markers of mental health illness, some of which include learning algorithms based on the heuristic of equally distributed balanced data. However, they yield biased results towards the majority data (i.e., normal behaviour). Thus, new perception is needed to explore the available data. This research deals with the first identification of such users from weblog data, and the similarity-based sampled data is then given to the classifier. The experiment analysis shows the effectiveness of this work and will provide the user's mental state information early to take timely necessary steps.
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