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 1351–1360 of 1706 papers

TitleStatusHype
An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation—0
Android Malware Detection Based on RGB Images and Multi-feature Fusion—0
An Effective Neural Network Model for Graph-based Dependency Parsing—0
An Efficient and Flexible Deep Learning Method for Signal Delineation via Keypoints Estimation—0
An Efficient Architecture for Predicting the Case of Characters using Sequence Models—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
An Empirical Study of Factors Affecting Language-Independent Models—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
An End-to-End Graph Convolutional Kernel Support Vector Machine—0
An Enhanced Ad Event-Prediction Method Based on Feature Engineering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified