A novel imbalanced data classification approach for suicidal ideation detection on social media

Learning from Imbalanced Data in Healthcare: State-of-the-Art and Research Challenges

D Roy, A Roy, U Roy - Computational Intelligence in Healthcare …, 2024 - Springer
Datasets associated with medical and healthcare domains are imbalanced in nature. An
imbalanced dataset refers to a classification dataset where the number of instances of a …
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Ranking Rules in Associative Classifiers via Borda's Methods

M Dall'Agnol, VO De Carvalho - 2023 18th Iberian Conference …, 2023 - ieeexplore.ieee.org
Associative classifiers have been widely used in many domains due to their inherent
interpretability. They are built in steps, one of them aimed at ranking the rules, usually …
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Clustering the Behavior of Objective Measures in Associative Classifiers

M Dall'Agnol, VO De Carvalho - 2023 18th Iberian Conference …, 2023 - ieeexplore.ieee.org
Associative classifiers (ACs) have been widely used in several domains due to their inherent
interpretability, with CBA being the most used algorithm in the family. They are built in steps …
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[PDF] researchsquare.com

DDdeep: deep learning-based text analysis for depression illness detection on social media posts

M reza Keyvanpour, S Mehrmolaei, F Gholami - 2022 - researchsquare.com
Recently, depression has been raised as one of the most popular mental health disorders in
the world. Also, social networks can be considered a valuable resource for mental health …
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[PDF] bracu.ac.bd

A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users

FBK Nahar, UH Afsana, AM Chowdhury, M Hasnaen… - 2023 - dspace.bracu.ac.bd
In this research, we propose a hybrid model for predicting suicide risk from text data that
incorporates BERT, VADER, and a Random Forest classifier for sentiment analysis. This …
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