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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 1351–1375 of 1706 papers

TitleStatusHype
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning—0
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings—0
Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning—0
Non-lexical neural architecture for fine-grained POS Tagging—0
Non-Linear Text Regression with a Deep Convolutional Neural Network—0
NOTE: Solution for KDD-CUP 2021 WikiKG90M-LSC—0
Novel Modelling Strategies for High-frequency Stock Trading Data—0
Novel Representation Learning Technique using Graphs for Performance Analytics—0
NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading—0
Obfuscated Memory Malware Detection—0
Object-Category Aware Reinforcement Learning—0
OCR Post-Processing Text Correction using Simulated Annealing (OPTeCA)—0
OmniGraph: Rich Representation and Graph Kernel Learning—0
On Designing Data Models for Energy Feature Stores—0
One button machine for automating feature engineering in relational databases—0
One-Shot Imitation Learning—0
OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification—0
Online Compact Convexified Factorization Machine—0
Online Conversation Disentanglement with Pointer Networks—0
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG—0
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning—0
On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification—0
On the Replicability and Reproducibility of Deep Learning in Software Engineering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified