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

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

Theano implementation of Cost-Sensitive Deep Neural Networks

A complete end-to-end fraud detection system for financial transactions, featuring data pipelines, cost-sensitive ML modeling, explainability with SHAP, threshold optimization, batch scoring, and an interactive Streamlit dashboard. Designed to simulate real-world fintech fraud-risk workflows.

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'

A hands-on lab showing how “improving” a single metric (AUC/accuracy/F1) can worsen real-world outcomes. Includes metric audits, slice checks, cost-sensitive evaluation, threshold tuning, and decision policies you can defend, so dashboards don’t quietly ship bad decisions.

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

End-to-end diabetes risk prediction pipeline (Pima): EDA → feature engineering → calibration + cost-aware threshold → deployable artifacts.

A genetic algorithm based approach for cost sensitive learning, in which the misclassification cost is considered together with the cost of feature extraction.

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

Deep Cost-sensitive Kernel Machine Model - PAKDD 2020

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.

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

Cost-aware credit card fraud detection pipeline: time-based split, probability calibration, and business-aligned threshold tuning (AUPRC-first).

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

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

Solution to the Data Mining Cup 2019 competition

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

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

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

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

Gastrointestinal diseases classification using Contrastive and Cost-sensitive Learning

Decision-grade donor outreach policy: calibrated P(donated_next_6m) scoring + budgeted Top-K actions + net-benefit optimal threshold, with exported deployable artifacts.

Machine learning model to predict loan defaults using the German Credit dataset. Features cost-sensitive classification, SHAP interpretability, and risk segmentation with 80.43% ROC-AUC. Built with Python, scikit-learn, XGBoost, and LightGBM.

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.

🧠COGNITIVA-AI: IA intermodal (clínica+MRI) para cribado temprano de Alzheimer; probabilidades calibradas, umbrales por cohorte (S2) y release reproducible.

This repository includes the analysis and report of a machine learning study created for an international academic conference IPCMC 2022.

Official repository for MSc Thesis. A Context-Aware Fraud Detection Prototype using Cost-Sensitive XGBoost on the IEEE-CIS dataset. Features Entity Resolution and behavioral velocity profiling to detect fraud without synthetic augmentation.