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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 1461–1470 of 1706 papers

TitleStatusHype
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques—0
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods—0
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks—0
Automatic Feature Engineering for Answer Selection and Extraction—0
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion—0
Automatic Features for Essay Scoring -- An Empirical Study—0
Automatic Inference of the Tense of Chinese Events Using Implicit Linguistic Information—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified