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 401–450 of 1706 papers

TitleStatusHype
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
AdvanceSplice: Integrating N-gram one-hot encoding and ensemble modeling for enhanced accuracyCode0
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations—0
Descriptive Kernel Convolution Network with Improved Random Walk KernelCode0
Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface—0
Unraveling the Key of Machine Learning Solutions for Android Malware Detection—0
Data organization limits the predictability of binary classification—0
A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data—0
GeoDecoder: Empowering Multimodal Map Understanding—0
Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic RegressionCode0
Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations—0
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores—0
Novel Representation Learning Technique using Graphs for Performance Analytics—0
Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness—0
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection—0
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection—0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
LLbezpeky: Leveraging Large Language Models for Vulnerability Detection—0
Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock—0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network—0
Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change—0
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting—0
Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning—0
USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification—0
Morphological Profiling for Drug Discovery in the Era of Deep Learning—0
Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning—0
Keyword spotting -- Detecting commands in speech using deep learning—0
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases—0
Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural NetworksCode0
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering—0
LiDAR-based Outdoor Crowd Management for Smart Campus on the Edge—0
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
Classification of Various Types of Damages in Honeycomb Composite Sandwich Structures using Guided Wave Structural Health MonitoringCode0
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support—0
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
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified