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 776–800 of 1706 papers

TitleStatusHype
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition—0
A Deep Learning Based Cost Model for Automatic Code Optimization—0
Individual Explanations in Machine Learning Models: A Case Study on Poverty Estimation—0
Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures—0
IoT Security: Botnet detection in IoT using Machine learning—0
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling—0
Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features—0
End-to-End Argument Mining as Biaffine Dependency Parsing—0
Mode Effects' Challenge to Authorship Attribution—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches—0
Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning—0
From Digital Humanities to Quantum Humanities: Potentials and Applications—0
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification—0
Word Embedding Techniques for Malware Evolution Detection—0
A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net—0
Decoding and interpreting cortical signals with a compact convolutional neural network—0
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?—0
DNN2LR: Automatic Feature Crossing for Credit Scoring—0
Robust PDF Document Conversion Using Recurrent Neural Networks—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
Geometric feature performance under downsampling for EEG classification tasks—0
Deep Learning Based Walking Tasks Classification in Older Adults using fNIRS—0
Feature Engineering for Scalable Application-Level Post-Silicon Debugging—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified