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 541–550 of 1706 papers

TitleStatusHype
Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation—0
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling—0
Amrita_CEN at SemEval-2022 Task 4: Oversampling-based Machine Learning Approach for Detecting Patronizing and Condescending Language—0
Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection—0
Few-shot incremental learning in the context of solar cell quality inspection—0
Using Person Embedding to Enrich Features and Data Augmentation for Classification—0
Vibration fault detection in wind turbines based on normal behaviour models without feature engineering—0
A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set—0
Efficient End-to-End AutoML via Scalable Search Space DecompositionCode1
Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified