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 1401–1425 of 1706 papers

TitleStatusHype
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—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
A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams—0
Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression LearningCode0
Spelling Correction as a Foreign Language—0
Shallow Updates for Deep Reinforcement Learning—0
Effective Representations of Clinical Notes—0
Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks—0
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions—0
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency ParsingCode0
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections—0
Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers—0
Joint RNN Model for Argument Component Boundary DetectionCode0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
Optimizing a PoS Tagset for Norwegian Dependency Parsing—0
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction PredictionCode0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Fast and Accurate Neural Word Segmentation for ChineseCode0
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction—0
An entity-driven recursive neural network model for chinese discourse coherence modeling—0
Fast Learning and Prediction for Object Detection using Whitened CNN Features—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified