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 726–750 of 1706 papers

TitleStatusHype
Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling—0
Application of federated learning techniques for arrhythmia classification using 12-lead ECG signals—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
Fake News Detection using Stance Classification: A Survey—0
Fake News Early Detection: An Interdisciplinary Study—0
Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide—0
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach—0
Dependency-based Gated Recursive Neural Network for Chinese Word Segmentation—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
Character Feature Engineering for Japanese Word Segmentation—0
Fast and Accurate Performance Analysis of LTE Radio Access Networks—0
Fast and Accurate Reordering with ITG Transition RNN—0
Fast Learning and Prediction for Object Detection using Whitened CNN Features—0
Automated detection of dark patterns in cookie banners: how to do it poorly and why it is hard to do it any other way—0
Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Unsupervised Learning—0
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction—0
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems—0
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M type classification—0
Feature Cross Search via Submodular Optimization—0
Feature Engineering and Classification Models for Partial Discharge in Power Transformers—0
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction—0
Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
An Efficient Architecture for Predicting the Case of Characters using Sequence Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified