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 326–350 of 1706 papers

TitleStatusHype
Introducing 3DCNN ResNets for ASD full-body kinematic assessment: a comparison with hand-crafted features—0
Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem—0
Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector—0
Understanding learning from EEG data: Combining machine learning and feature engineering based on hidden Markov models and mixed modelsCode0
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks—0
Auto deep learning for bioacoustic signalsCode0
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support—0
Classification of Various Types of Damages in Honeycomb Composite Sandwich Structures using Guided Wave Structural Health MonitoringCode0
Leveraging sinusoidal representation networks to predict fMRI signals from EEG—0
CLIP-Motion: Learning Reward Functions for Robotic Actions Using Consecutive Observations—0
RELand: Risk Estimation of Landmines via Interpretable Invariant Risk MinimizationCode0
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
A Data-driven Deep Learning Approach for Bitcoin Price Forecasting—0
netFound: Foundation Model for Network SecurityCode1
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes—0
Personalized human mobility prediction for HuMob challenge—0
A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading—0
A Novel Statistical Measure for Out-of-Distribution Detection in Data Quality Assurance—0
Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution—0
A novel Network Science Algorithm for Improving Triage of Patients—0
Lightweight Boosting Models for User Response Prediction Using Adversarial ValidationCode0
FASER: Binary Code Similarity Search through the use of Intermediate RepresentationsCode1
Enhanced LFTSformer: A Novel Long-Term Financial Time Series Prediction Model Using Advanced Feature Engineering and the DS Encoder Informer Architecture—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified