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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 1581–1590 of 1706 papers

TitleStatusHype
Cognito: Automated Feature Engineering for Supervised Learning—0
Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty—0
Named Entity Recognition with Bidirectional LSTM-CNNsCode0
Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural NetworksCode0
Performing Highly Accurate Predictions Through Convolutional Networks for Actual Telecommunication Challenges—0
Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features—0
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding—0
OmniGraph: Rich Representation and Graph Kernel Learning—0
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation—0
Probabilistic Bag-Of-Hyperlinks Model for Entity LinkingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified