Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.

Theano implementation of Cost-Sensitive Deep Neural Networks

Value-driven and cost-sensitive analysis for scikit-learn

This repo contains implementation of advanced ML techniques. Includes model ensembles, cost-sensitive learning and dealing with class imbalance.

Pytorch implementation for paper 'BANNER: A Cost-Sensitive Contextualized Model for Bangla Named Entity Recognition'

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.

A python implementation of a genetic algorithm based approach for 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.

Deep Cost-sensitive Kernel Machine Model - PAKDD 2020

Implementation of cost sensitive KNN algorithm described in Qin, et al, 2013

Predicting whether an African country will be in recession or not with advanced machine learning techniques involving class imbalance, cost-sensitive learning and explainable machine learning

Solution to the Data Mining Cup 2019 competition

Official code for our paper - "Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data".

Worked on detecting illicit transactions in the Ethereum Transactions dataset by increasing our dataset size, and with little tolerance to missing fraudulent transactions.

This work focuses on the development of machine learning models, in particular neural networks and SVM, where they can detect toxicity in comments. The topics we will be dealing with: a) Cost-sensitive learning, b) Class imbalance

A cost-sensitive BERT that handles the class imbalance for the task of biomedical NER.

A machine learning project addressing credit card fraud detection using imbalanced datasets. Utilizes techniques like cost-sensitive learning, SMOTE, and ensemble models for high precision and accuracy, emphasizing robust performance despite challenging data distributions.

Paper under review on "Multimedia Tools and Applications" journal.

To solve two main issues in credit card fraud detection - skewness of the data and cost-sensitivity

Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"