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 1121–1130 of 1706 papers

TitleStatusHype
If You Can't Beat Them Join Them: Handcrafted Features Complement Neural Nets for Non-Factoid Answer Reranking—0
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks—0
Image-Based Malware Classification Using QR and Aztec Codes—0
Image Retrieval and Pattern Spotting using Siamese Neural Network—0
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures—0
Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network—0
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
Improved Differential Evolution based Feature Selection through Quantum, Chaos, and Lasso—0
Improved Semantic Parsers For If-Then Statements—0
Improved Sentence-Level Arabic Dialect Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified