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Feature Engineering
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Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.
The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pcerda/string_categorical_encoders
공식 코드https://github.com/pcwhy/xml-intrusiondetection
공식 코드Explainable AI for Comparative Analysis of Intrusion Detection Models
https://github.com/peterdsharpe/neuralfoil
공식 코드NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
https://github.com/petermattia/revisit-severson-et-al
공식 코드Statistical learning for accurate and interpretable battery lifetime prediction
https://github.com/pfnet-research/CSDI_T
공식 코드Diffusion models for missing value imputation in tabular data
https://github.com/philipboeken/debiased_regression
공식 코드Correcting for Selection Bias and Missing Response in Regression using Privileged Information
https://github.com/pientist/ballradar
공식 코드https://github.com/pingala-institute/Bayes-medicaldataimputation
공식 코드Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation
https://github.com/pingayush/INTELLIGUARD-Enhancing-Network-Security
공식 코드https://github.com/pinkeshbadjatiya/neuralTextSegmentation
공식 코드https://github.com/pnnl/safekit
공식 코드Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
https://github.com/poclecoqq/slr-datacleaning
공식 코드Data Cleaning and Machine Learning: A Systematic Literature Review
https://github.com/pondruska/DeepTracking
공식 코드Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks
https://github.com/pouyapez/mkbe
공식 코드Embedding Multimodal Relational Data for Knowledge Base Completion
https://github.com/pranav-chib/trajimpute
공식 코드Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking
https://github.com/prasenjit52282/dalton-dataset
공식 코드Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities
https://github.com/prdwb/UserIntentPrediction
공식 코드https://github.com/princengoc/s4s-final
공식 코드Improving random walk rankings with feature selection and imputation
https://github.com/pro-genai/lml-dap
공식 코드LML-DAP: Language Model Learning a Dataset for Data-Augmented Prediction
https://github.com/probabilistic-learning/HI-VAE
공식 코드https://github.com/probcomp/trcrpm
공식 코드https://github.com/pwwl/enhanced-binary-diversification
공식 코드Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
https://github.com/pydsgz/MGMC
공식 코드https://github.com/pyyeast/yeast-morphology-tutorial
공식 코드Quantifying yeast colony morphologies with feature engineering from time-lapse photography
https://github.com/qiliuchn/data-corruption-study
공식 코드https://github.com/qubilab/AnDi_ELM
공식 코드Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories
https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation
공식 코드https://github.com/rafaelvalle/MDI
공식 코드https://github.com/rafidrm/gancer
공식 코드Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks
https://github.com/raghavian/lungVAE
공식 코드Lung Segmentation from Chest X-rays using Variational Data Imputation
https://github.com/rajeshrinet/pyross
공식 코드Age-structured impact of social distancing on the COVID-19 epidemic in India
https://github.com/ram-g-athreya/RNN-Question-Answering
공식 코드Template-based Question Answering using Recursive Neural Networks
https://github.com/rashaalshawi/dual-attention-u-net-with-feature-infusion-pushing-the-boundaries-of-multiclass-defect-segmentation
공식 코드Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
https://github.com/ratschlab/GP-VAE
공식 코드https://github.com/rbundschuh/CLEAR
공식 코드CLEAR: Coverage-based Limiting-cell Experiment Analysis for RNA-seq
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.