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 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
eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems—0
Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation—0
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks—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
Estimating Brain Age with Global and Local Dependencies—0
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification—0
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models—0
A Numbers Game: Numeric Encoding Options with Automunge—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—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
Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem—0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change—0
Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization—0
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified