Synthetic Minority Over-sampling Technique
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Updated
Mar 27, 2017 - Python
Synthetic Minority Over-sampling Technique
This script of code helped oversampling the data for getting a balanced data set
Develop predictive models that can determine, given a particular compound, whether it is active (1) or not (0).
Predicting the appropriate star ratings for the text reviews of Amazon movies and TV shows using Natural Language Processing and methods like Multinomial Naive Bayes and Logistic Regression.
Dealing with class imbalance problem in machine learning. Synthetic oversampling(SMOTE, ADASYN).
📝 ML Paper implementation of machine learning paper, ADASYN
Experiments conducted on the TPEHGDB dataset to reproduce the reported results from "A critical look at studies applying over-sampling on the TPEHGDB dataset"
Utility package to help in oversampling images in Image Classification tasks
Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"
This is an end-to-end machine learning model in which I implement random-forest and decision tree classifiers to predict heart disease. I utilized cross-validation, and oversampling to deal with an imbalanced dataset.
A minority oversampling method for imbalance data set
The computing scripts associated with our paper entitled "Oversampling Highly Imbalanced Indoor Positioning Data using Deep Generative Models".
Cervical Cancer risk factor prediction.
🎲 Iterable dataset resampling in PyTorch
RNN-based security patch identification with oversampling samples. This is an extension code in the MILCOM'21 paper "PatchRNN: A Deep Learning-Based System for Security Patch Identification".
Patch oversampling (synthesis) with direct patch analysis. This is an alternative solution to the PatchOversampling repository, providing a simpler and more direct way to synthesize patches. The original oversampling method is described in the DSN'21 paper "PatchDB: A Large-Scale Security Patch Dataset".
Data clearance for security patches and non-security patches. This method is described as Nearest Link Search in the paper "PatchDB: A Large-Scale Security Patch Dataset", which appears in 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2021), Taipei, June 21-24, 2021, pp. 149-160.
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