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 1131–1140 of 1706 papers

TitleStatusHype
Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations—0
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
Improving Citation Polarity Classification with Product Reviews—0
Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning—0
Improving extreme weather events detection with light-weight neural networks—0
Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate Variability—0
Improving Neural Translation Models with Linguistic Factors—0
Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores—0
Improving Radiography Machine Learning Workflows via Metadata Management for Training Data Selection—0
Improving Representation Learning of Complex Critical Care Data with ICU-BERT—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified