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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 876–900 of 1706 papers

TitleStatusHype
Neural Networks Leverage Corpus-wide Information for Part-of-speech Tagging—0
Hybrid Neural Tagging Model for Open Relation Extraction—0
Neural Recovery Machine for Chinese Dropped Pronoun—0
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)—0
NeurIPS 2024 Ariel Data Challenge: Characterisation of Exoplanetary Atmospheres Using a Data-Centric Approach—0
Neurology-as-a-Service for the Developing World—0
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions—0
Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning—0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified