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 1341–1350 of 1706 papers

TitleStatusHype
A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set—0
A Multi-task Approach to Predict Likability of Books—0
A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Machine Learning for Wireless Link Quality Estimation: A Survey—0
Analysis of Rhythmic Phrasing: Feature Engineering vs. Representation Learning for Classifying Readout Poetry—0
Analyzing Multispectral Satellite Imagery of South American Wildfires Using Deep Learning—0
An Analysis of Encoder Representations in Transformer-Based Machine Translation—0
A Natural Language Processing Approach to Malware Classification—0
An AutoML-based approach for Network Intrusion Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified