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 1001–1010 of 1706 papers

TitleStatusHype
Plasmodium Detection Using Simple CNN and Clustered GLCM Features—0
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison—0
Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning—0
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained EnvironmentsCode0
Machine Learning Pipelines with Modern Big Data Tools for High Energy PhysicsCode0
Deep learning approach to control of prosthetic hands with electromyography signals—0
Slices of Attention in Asynchronous Video Job Interviews—0
Statistical and machine learning ensemble modelling to forecast sea surface temperature—0
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure DetectionCode0
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified