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 551–560 of 1706 papers

TitleStatusHype
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
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning—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
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
Detection of Product Comparisons - How Far Does an Out-of-the-Box Semantic Role Labeling System Take You?—0
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network—0
An Empirical Study of Factors Affecting Language-Independent Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified