SOTAVerified

Feature Engineering

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.

Papers

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Showing 476–500 of 1706 papers

TitleStatusHype
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral EnhancementCode0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Augmenting data-driven models for energy systems through feature engineering: A Python framework for feature engineering—0
xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactionsCode0
Online learning techniques for prediction of temporal tabular datasets with regime changesCode1
Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques—0
Toward Efficient Automated Feature Engineering—0
ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight ConditionsCode0
Pushing the boundaries of molecular property prediction for drug discovery with multitask learning BERT enhanced by SMILES enumerationCode1
Spatially-resolved Thermometry from Line-of-Sight Emission Spectroscopy via Machine Learning—0
Improving Warped Planar Object Detection Network For Automatic License Plate Recognition—0
Tool flank wear prediction using high-frequency machine data from industrial edge device—0
ML-powered KQI estimation for XR services. A case study on 360-Video—0
Mitigating Spurious Correlations for Self-supervised RecommendationCode0
Neighborhood Adaptive Estimators for Causal Inference under Network Interference—0
Bi-LSTM Price Prediction based on Attention Mechanism—0
Intent Recognition in Conversational Recommender Systems—0
Feature Selection with Distance Correlation—0
Novel Modelling Strategies for High-frequency Stock Trading Data—0
Deduplication Over Heterogeneous Attribute Types (D-HAT)Code0
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking AlgorithmsCode1
CASPR: Customer Activity Sequence-based Prediction and RepresentationCode1
Photometric identification of compact galaxies, stars and quasars using multiple neural networksCode0
Small Language Models for Tabular DataCode0
A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified