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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 1251–1300 of 1706 papers

TitleStatusHype
PyRATA, Python Rule-based feAture sTructure AnalysisCode0
Attention for Implicit Discourse Relation Recognition—0
Reusable workflows for gender prediction—0
RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering—0
DeepTriangle: A Deep Learning Approach to Loss ReservingCode0
Data-Driven Investigative Journalism For Connectas Dataset—0
A machine learning model for identifying cyclic alternating patterns in the sleeping brain—0
False Information on Web and Social Media: A SurveyCode0
Event Extraction with Generative Adversarial Imitation Learning—0
A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction—0
Learning to Extract Coherent Summary via Deep Reinforcement Learning—0
Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition—0
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR PredictionCode0
A Simple and Effective Approach to the Story Cloze Test—0
PotentialNet for Molecular Property Prediction—0
An Unsupervised Model with Attention Autoencoders for Question Retrieval—0
Extractive Text Summarization using Neural Networks—0
Interpreting Complex Regression Models—0
Global Pose Estimation with an Attention-based Recurrent Network—0
Event Nugget Detection with Forward-Backward Recurrent Neural Networks—0
Democratizing AI: Non-expert design of prediction tasks—0
client2vec: Towards Systematic Baselines for Banking Applications—0
Predicting Customer Churn: Extreme Gradient Boosting with Temporal DataCode0
URLNet: Learning a URL Representation with Deep Learning for Malicious URL DetectionCode0
Online Compact Convexified Factorization Machine—0
Heuristic Feature Selection for Clickbait Detection—0
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task LearningCode0
Evaluating approaches for supervised semantic labelingCode0
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition—0
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions—0
A Pipeline for Post-Crisis Twitter Data Acquisition—0
Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics—0
Neural Feature Learning From Relational Database—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching ModelCode0
Learning Feature Representations for Keyphrase Extraction—0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
Learning Representations from Road Network for End-to-End Urban Growth Simulation—0
ATM: A distributed, collaborative, scalable system for automated machine learningCode0
On the Benefit of Combining Neural, Statistical and External Features for Fake News IdentificationCode0
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical PropertiesCode0
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly DetectionCode0
OCR Post-Processing Text Correction using Simulated Annealing (OPTeCA)—0
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading—0
Spherical convolutions and their application in molecular modelling—0
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion—0
AutoLearn - Automated Feature Generation and SelectionCode0
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified