A cost-sensitive BERT that handles the class imbalance for the task of biomedical NER.
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Updated
Apr 11, 2024 - Python
A cost-sensitive BERT that handles the class imbalance for the task of biomedical NER.
This project endeavors to synthesize the challenges posed by varying misclassification costs and class imbalances, along with the corresponding solutions available for addressing these issues.
Noise Identification, Noise reduction, and Sentiment Analysis on Bangla Noisy Texts
Final project for Data Mining course (Uniba)
Software implementation of a manuscript submitted to Information Sciences
Gastrointestinal disease classification using Contrastive and Cost-sensitive Learning
Gastrointestinal diseases classification using Contrastive and Cost-sensitive Learning
A genetic algorithm based approach for cost sensitive learning, in which the misclassification cost is considered together with the cost of feature extraction.
Credit Scoring Course: Module
Worked on detecting illicit transactions in the Ethereum Transactions dataset by increasing our dataset size, and with little tolerance to missing fraudulent transactions.
Cost Sensitive Learning in German Credit Data
Paper under review on "Multimedia Tools and Applications" journal.
This repository includes the analysis and report of a machine learning study created for an international academic conference IPCMC 2022.
Official code for our paper - "Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data".
R package for dealing with cost-sensitive learning (class imbalance and classification error cost) in a multiclass setting using lasso regularized logistic regression and gradient boosted decision trees.
Proposed assignment notebooks for Advanced Topics in Machine Learning tasks
Advanced Machine Learning Algorithms including Cost-Sensitive Learning, Class Imbalances, Multi-Label Data, Multi-Instance Learning, Active Learning, Multi-Relational Data Mining, Interpretability in Python using Scikit-Learn.
Dementia Prediction by Khalil El Asmar, Fatima Abu Salem, Hiyam Ghannam, Roaa Al-Feel
Most existing classification approaches assume the underlying training set is evenly distributed but many real-world classification problems have an imbalanced class distribution, such as rare disease identification, fraud detection, spam detection, churn prediction, electricity theft & pilferage etc.
Fall 2020 - Computational Medicine - course project
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