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 1371–1380 of 1706 papers

TitleStatusHype
An investigation of a deep learning based malware detection system—0
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection—0
A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task—0
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals—0
A novel Network Science Algorithm for Improving Triage of Patients—0
A Novel Statistical Measure for Out-of-Distribution Detection in Data Quality Assurance—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
A Numbers Game: Numeric Encoding Options with Automunge—0
An Unsupervised Model with Attention Autoencoders for Question Retrieval—0
AnyThreat: An Opportunistic Knowledge Discovery Approach to Insider Threat Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified