Treers / MetaCost
P. Domingos proposed a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. The procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it.
☆39Updated 5 years ago
Alternatives and similar repositories for MetaCost:
Users that are interested in MetaCost are comparing it to the libraries listed below
- [ICDE'20] ⚖️ A general, efficient ensemble framework for imbalanced classification. | 泛用,高效,鲁棒的类别不平衡学习框架☆257Updated last year
- [NeurIPS’20] ⚖️ Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题☆106Updated 10 months ago
- ☆37Updated 4 years ago
- A python implementation of a genetic algorithm based approach for cost sensitive learning☆8Updated 5 years ago
- ☆15Updated 2 years ago
- Our implementations of the Multi-class Imbalance learning algorithms (for the KBS paper)☆46Updated 6 years ago
- XGBoost for label-imbalanced data: XGBoost with weighted and focal loss functions☆320Updated last year
- Oversampling for imbalanced learning based on k-means and SMOTE☆126Updated 3 years ago
- Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.☆107Updated 4 years ago
- Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/☆77Updated 11 months ago
- Deep Feature Selection using Teacher Student Network☆43Updated 5 years ago
- A missing value imputation library based on machine learning. It's implementation missForest, simple edition of MICE(R pacakge), knn, EM,…☆107Updated last year
- Tutorial on cost-sensitive boosting and calibrated AdaMEC.☆26Updated 7 years ago
- Oversampling method based on relative density☆11Updated 4 years ago
- 🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库☆357Updated last month
- This method is a new oversampling algorithm and can circumvent the deficiency of WK-SMOTE (and SMOTE as well as its variants) caused by r…☆16Updated 2 years ago
- Using Imblearn To Tackle Imbalanced Data Sets☆37Updated 8 years ago
- A Particle Swarm Optimization (PSO) for Feature Selection. Using PySwarm.☆53Updated 6 years ago
- Infinite Feature Selection: a Graph-based Feature Filtering Approach☆40Updated 9 months ago
- An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems☆251Updated 5 years ago
- A pytorch implementation of "SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data"☆29Updated 5 years ago
- 常用的特征选择方法☆68Updated 2 years ago
- Radial-Based Undersampling for Imbalanced Data Classification☆12Updated 5 years ago
- Houses implementation of the Fast Correlation-Based Filter (FCBF) feature selection method.☆61Updated 3 years ago
- Supplementary material for IJCNN paper "XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning"☆82Updated 5 years ago
- Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics☆18Updated 2 years ago
- (Python, R) Cost-sensitive multiclass classification (Weighted-All-Pairs, Filter-Tree & others)☆48Updated 3 months ago
- The stream-learn is an open-source Python library for difficult data stream analysis.☆63Updated last month
- ☆22Updated 5 years ago
- Fast Correlation-Based Feature Selection☆31Updated 7 years ago