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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 826–850 of 1706 papers

TitleStatusHype
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
Show:102550
← PrevPage 34 of 69Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified