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 141–150 of 1706 papers

TitleStatusHype
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
Deep learning approach to control of prosthetic hands with electromyography signals—0
A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo—0
A Multi-task Approach to Predict Likability of Books—0
A Dual-Layer Semantic Role Labeling System—0
A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set—0
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task—0
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level—0
A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data—0
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified