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 551–575 of 1706 papers

TitleStatusHype
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
Dynamic Feature Induction: The Last Gist to the State-of-the-Art—0
Streaming Adaptive Nonparametric Variational Autoencoder—0
Differentiable Sparsification for Deep Neural Networks—0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers—0
Diagnosis of Parkinson's Disease Using EEG Signals and Machine Learning Techniques: A Comprehensive Study—0
Determining whether the non-protein-coding DNA sequences are in a complex interactive relationship by using an artificial intelligence method—0
Differentiable Sparsification for Deep Neural Networks—0
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Discourse Parsing with Attention-based Hierarchical Neural Networks—0
Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?—0
Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques—0
An End-to-End Graph Convolutional Kernel Support Vector Machine—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Distinguishing Risk Preferences using Repeated Gambles—0
Distributed Multi-Head Learning Systems for Power Consumption Prediction—0
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network—0
An Empirical Study of Factors Affecting Language-Independent Models—0
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence—0
DNN2LR: Automatic Feature Crossing for Credit Scoring—0
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified