Theano implementation of Cost-Sensitive Deep Neural Networks
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Updated
Jun 1, 2018 - Python
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.
To solve two main issues in credit card fraud detection - skewness of the data and cost-sensitivity
Implementation of cost sensitive KNN algorithm described in Qin, et al, 2013
A python implementation of a genetic algorithm based approach for cost sensitive learning
Solution to the Data Mining Cup 2019 competition
Pytorch implementation for paper 'BANNER: A Cost-Sensitive Contextualized Model for Bangla Named Entity Recognition'
Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"
Weka implementation of the cost-sensitive decision forest algorithm CSForest.
Deep Cost-sensitive Kernel Machine Model - PAKDD 2020
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
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.
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 / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification
A python class for making machine learning algorithms cost sensitive.
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
Fall 2020 - Computational Medicine - course project
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.
Dementia Prediction by Khalil El Asmar, Fatima Abu Salem, Hiyam Ghannam, Roaa Al-Feel
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