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 1101–1125 of 1706 papers

TitleStatusHype
High-Order Low-Rank Tensors for Semantic Role Labeling—0
HMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection—0
HONEM: Learning Embedding for Higher Order Networks—0
Cost-Efficient Prompt Engineering for Unsupervised Entity Resolution—0
How to Use less Features and Reach Better Performance in Author Gender Identification—0
HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis—0
HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets—0
Human heuristics for AI-generated language are flawed—0
HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition—0
Hunting Tomorrow's Leaders: Using Machine Learning to Forecast S&P 500 Additions & Removal—0
Hybridization of Persistent Homology with Neural Networks for Time-Series Prediction: A Case Study in Wave Height—0
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization—0
ICE: Information Credibility Evaluation on Social Media via Representation Learning—0
ICL’s Submission to the WMT21 Critical Error Detection Shared Task—0
ICRC-HIT: A Deep Learning based Comment Sequence Labeling System for Answer Selection Challenge—0
Identifying Clickbait: A Multi-Strategy Approach Using Neural Networks—0
Identifying Protein-protein Interactions in Biomedical Literature using Recurrent Neural Networks with Long Short-Term Memory—0
Identifying Real Estate Opportunities using Machine Learning—0
Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach—0
Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study—0
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking—0
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks—0
Image-Based Malware Classification Using QR and Aztec Codes—0
Image Retrieval and Pattern Spotting using Siamese Neural Network—0
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified