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 621–630 of 1706 papers

TitleStatusHype
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning—0
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
Effort Estimation in Named Entity Tagging Tasks—0
Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans—0
Bayesian Kernel Methods for Natural Language Processing—0
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks—0
End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified