🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库 [NeurIPS'25]
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Updated
Mar 5, 2026 - Python
🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库 [NeurIPS'25]
[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
Open solution to the Toxic Comment Classification Challenge
This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.
A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python
Semi-supervised anomaly detection method
Open solution to the Santander Value Prediction Challenge 🐠
AICUP 2024 Cross-camera Multiple-object tracking
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"
Bayesian Reward Shaping Framework for Deep Reinforcement Learning
Fake News Detection - Feature Extraction using Vectorization such as Count Vectorizer, TFIDF Vectorizer, Hash Vectorizer,. Then used an Ensemble model to classify whether the news is fake or not.
Feature selection method based on repeated elastic net.
The code of Team Rhinobird for Mining the Web of HTML-embedded Product Data Task One at ISWC2020
A scikit-learn-compatible module for Isolation-based anomaly detection using nearest-neighbor ensembles
WiFi CSI-based gesture recognition using dual-path ensemble deep learning (CNN2D + CNN1D-LSTM). 90.19% accuracy on ESP32 hardware with 426K parameters.
Code to implement the Runner-up Solution to the CVPR'23 SoccerNet Challenge (Ball Action Spotting Task)
This repo contains various data science strategy and machine learning models to deal with structure as well as unstructured data. It contains module on feature-preprocessing, feature-engineering, machine-learning-models, bayesian-parameter-tuning, etc, built using libraries such as scikit-learn, keras, h2o, xgboost, lightgbm, catboost, etc.
Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution
Multiple Model Ensembling
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