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 1151–1160 of 1706 papers

TitleStatusHype
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
Reuse and Adaptation for Entity Resolution through Transfer Learning—0
Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
Lexical Bias In Essay Level Prediction—0
A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine—0
An investigation of a deep learning based malware detection system—0
Identifying Real Estate Opportunities using Machine Learning—0
Measuring Systematic Risk with Neural Network Factor Model—0
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified