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 801–850 of 1706 papers

TitleStatusHype
Free-Text Keystroke Dynamics for User Authentication—0
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations—0
From Digital Humanities to Quantum Humanities: Potentials and Applications—0
From Features to Transformers: Redefining Ranking for Scalable Impact—0
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction—0
Gated Recursive and Sequential Deep Hierarchical Encoding for Detecting Incongruent News Articles—0
Gated Recursive Neural Network for Chinese Word Segmentation—0
GBD-NER at PARSEME Shared Task 2018: Multi-Word Expression Detection Using Bidirectional Long-Short-Term Memory Networks and Graph-Based Decoding—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
GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model—0
Generalized Convolutional Neural Networks for Point Cloud Data—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation—0
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way—0
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction—0
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
GeoDecoder: Empowering Multimodal Map Understanding—0
Geomancer: An Open-Source Framework for Geospatial Feature Engineering—0
Geometric feature performance under downsampling for EEG classification tasks—0
Getting the Most out of AMR Parsing—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
An Efficient Architecture for Predicting the Case of Characters using Sequence Models—0
Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition—0
Global Pose Estimation with an Attention-based Recurrent Network—0
Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network—0
GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text—0
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees—0
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT—0
HMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection—0
Graph-based Dependency Parsing with Bidirectional LSTM—0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
GraphCNNpred: A stock market indices prediction using a Graph based deep learning system—0
Combining Tree Structures, Flat Features and Patterns for Biomedical Relation Extraction—0
Article citation study: Context enhanced citation sentiment detection—0
Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution—0
ABM: an automatic supervised feature engineering method for loss based models based on group and fused lasso—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization—0
Graph Neural Networks and Boolean Satisfiability—0
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction—0
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Artificial Intelligence for Diabetes Case Management: The Intersection of Physical and Mental Health—0
GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent—0
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores—0
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified