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 881–890 of 1706 papers

TitleStatusHype
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Distinguishing Risk Preferences using Repeated Gambles—0
Distributed Multi-Head Learning Systems for Power Consumption Prediction—0
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence—0
DNN2LR: Automatic Feature Crossing for Credit Scoring—0
Do LSTMs really work so well for PoS tagging? -- A replication study—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Downsampling and geometric feature methods for EEG classification tasks with CNNs—0
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling—0
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified