There are 5 repositories under cost-sensitive-learning topic.
Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.
Theano implementation of Cost-Sensitive Deep Neural Networks
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 python implementation of a genetic algorithm based approach for cost sensitive learning
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 genetic algorithm based approach for cost sensitive learning, in which the misclassification cost is considered together with the cost of feature extraction.
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
Official code for our paper - "Melanoma classification from dermatoscopy images using knowledge distillation for highly imbalanced data".
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
Gastrointestinal diseases classification using Contrastive and Cost-sensitive Learning
Software to build Decision Trees for imbalanced data. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001242
To solve two main issues in credit card fraud detection - skewness of the data and cost-sensitivity
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.
Help you interact with different packages regarding imbalanced-learning, and extend them to search for a best model
This repository includes the analysis and report of a machine learning study created for an international academic conference IPCMC 2022.
Credit Scoring Course: Module
Paper under review on "Multimedia Tools and Applications" journal.
Cost Sensitive Learning in German Credit Data
Software implementation of a manuscript submitted to Information Sciences
Dementia Prediction by Khalil El Asmar, Fatima Abu Salem, Hiyam Ghannam, Roaa Al-Feel
Proposed assignment notebooks for Advanced Topics in Machine Learning tasks
Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"
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.
Final project for Data Mining course (Uniba)
Repo contains scripts to perform data analysis on structure data. It also provides a comparison of various ML algorithms at different stages of data preparation.
Noise Identification, Noise reduction, and Sentiment Analysis on Bangla Noisy Texts
Fall 2020 - Computational Medicine - course project
Gastrointestinal disease classification using Contrastive and Cost-sensitive Learning
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification