SOTAVerified

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 1376–1400 of 1706 papers

TitleStatusHype
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles—0
A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings SurprisesCode0
Hyperbolic Representation Learning for Fast and Efficient Neural Question AnsweringCode0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
Learning to Rank Question Answer Pairs with Holographic Dual LSTM ArchitectureCode0
Generalized Convolutional Neural Networks for Point Cloud Data—0
Automation of Feature Engineering for IoT Analytics—0
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network—0
Varying Linguistic Purposes of Emoji in (Twitter) Context—0
Predicting Depression for Japanese Blog Text—0
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering—0
A Local Detection Approach for Named Entity Recognition and Mention Detection—0
Deep Learning in Semantic Kernel Spaces—0
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognitionCode0
Interpretable Predictions of Tree-based Ensembles via Actionable Feature TweakingCode0
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR ModelsCode0
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—0
Recognizing irregular entities in biomedical text via deep neural networks—0
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem—0
One button machine for automating feature engineering in relational databases—0
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget—0
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning—0
Robust Tracking Using Region Proposal Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified