wangxiumei

wangxiumei's Stars

• Diego999/pyGAT

  Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

  Language:Python2.9k1771687

• FighterLYL/GraphNeuralNetwork

  《深入浅出图神经网络：GNN原理解析》配套代码

  Language:Jupyter Notebook1.7k2757457

• wyharveychen/CloserLookFewShot

  source code to ICLR'19, 'A Closer Look at Few-shot Classification'

  Language:Python1.1k1974269

• NLPScott/bert-Chinese-classification-task

  bert中文分类实践

  Language:Python7343028224

• smazzanti/mrmr

  mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

  Language:Python51484178

• cod3licious/autofeat

  Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

  Language:Python495193660

• ziyujia/Physiological-Signal-Classification-Papers

  A list of papers for physiological signal classification using machine learning/deep learning.

  2147137

• mims-harvard/Raindrop

  Graph Neural Networks for Irregular Time Series

  Language:Python16471334

• krishnaik06/Handle-Imbalanced-Dataset

  Language:Jupyter Notebook11860151

• alfonmedela/triplet-loss-pytorch

  Highly efficient PyTorch version of the Semi-hard Triplet loss ⚡️

  Language:Python1145318

• luanshiyinyang/Stacking

  机器学习集成模型之Stacking各类模型及工具源码

  Language:Python1001019

• yazanobeidi/fraud-detection

  Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

  Language:Jupyter Notebook995241

• Vettejeep/MSDS696-Masters-Final-Project

  Earthquake Prediction Challenge with LightGBM and XGBoost

  Language:Jupyter Notebook662027

• pievos101/GNN-SubNet

  Disease subnetwork detection with explainable Graph Neural Networks

  Language:Python21524

• ideasplus/imbalance-learning

  There are some reproduced algorithms for learning from imbalanced data, including over-sampling,under-sampling and boosting

  Language:Python11201

• mac-n/Clustering-GNN

  Code for 'Alzheimer’s Disease Classification Using Cluster-based Labelling for Graph Neural Network on Tau PET Imaging and Heterogeneous Data '

  Language:Jupyter Notebook101

• xiangzhang1015/ML_BCI_tutorial

  Language:Jupyter Notebook100

• nbswords/Ranking-Titanic-with-Recursive-Feature-Elimination

  A practice of Recursive Feature Elimination (RFE) using Titanic dataset

  Language:Jupyter Notebook9201

• usarawgi911/Uncertainty-aware-boosting

  Language:Python9311

• mhlee216/Biodegradability_Prediction_QSAR_GCN

  Code for "A Comparative Study of the Performance for Predicting Biodegradability Classification: The Quantitative Structure–Activity Relationship Model vs the Graph Convolutional Network" (https://doi.org/10.1021/acsomega.1c06274)

  Language:Jupyter Notebook8213

• tanishqgautam/ML-Algorithms-for-Breast-Cancer-Prediction

  Application of several machine learning techniques to classify whether the tumor mass is benign or malignant in women residing in the state of Wisconsin, USA.

  Language:Jupyter Notebook8104

• nankris/Recursive-Feature-elimination-RFE-using-cross-validation

  RFE on housing dataset

  Language:Jupyter Notebook62

• itsmeafra/Sublevel-Set-TDA

  Language:Jupyter Notebook3302

• abhilash1910/NexGCN

  Sigmoid Spectral GCN library for binary classification on Networkx Graphs using statistical (centrality,distributions)/external features

  Language:Jupyter Notebook221

• Manidills/RFE

  Feature Selection

  Language:Jupyter Notebook2200

• zhang946/Deep-Dual-Side-Learning-Ensemble-Model-for-Parkinson-Speech-Recognition

  Deep Dual-Side Learning Ensemble Model for Parkinson Speech Recognition

  Language:MATLAB2

• arpitkhare144/ipldataset

  creating heatmaps, scatter plots, and clustering of the points with the help of various libraries of python.

  Language:Jupyter Notebook1

• christopher9509/heart_graph

  heart disease prediction task

  Language:Jupyter Notebook1

• francescoferrini/GNN-for-binary-classification-on-social-interactions-datasets

  Language:Jupyter Notebook1

• zoeyejiseoung/CreditCard_Churn

  I built a gradient boosting model pipeline. I analyzed data, created visualizations, engineered features, performed oversampling with Adasyn, and find the best hyperameters with Optuna

  Language:Jupyter Notebook11