A python class for making machine learning algorithms cost sensitive.
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Updated
Apr 20, 2021
A python class for making machine learning algorithms cost sensitive.
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.
Dementia Prediction by Khalil El Asmar, Fatima Abu Salem, Hiyam Ghannam, Roaa Al-Feel
Credit Scoring Course: Module
Software implementation of a manuscript submitted to Information Sciences
Proposed assignment notebooks for Advanced Topics in Machine Learning tasks
Fall 2020 - Computational Medicine - course project
Noise Identification, Noise reduction, and Sentiment Analysis on Bangla Noisy Texts
Gastrointestinal disease classification using Contrastive and Cost-sensitive Learning
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification
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.
Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"
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.
Software to build Decision Trees for imbalanced data. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001242
Cost Sensitive Learning in German Credit Data
Paper under review on "Multimedia Tools and Applications" journal.
Weka implementation of the cost-sensitive decision forest algorithm CSForest.
This repository includes the analysis and report of a machine learning study created for an international academic conference IPCMC 2022.
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