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 701–725 of 1706 papers

TitleStatusHype
Explainable Automatic Grading with Neural Additive Models—0
Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Explainable Neural Networks based on Additive Index Models—0
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering—0
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
Exploiting Meta-Cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation—0
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish—0
Exploration of Proximity Heuristics in Length Normalization—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems—0
Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations—0
Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer—0
Exploring Patterns Behind Sports—0
Exploring Representations from Unlabeled Data with Co-training for Chinese Word Segmentation—0
Extended pipeline for content-based feature engineering in music genre recognition—0
Extracting Drug-Drug Interactions with Attention CNNs—0
Automated Essay Scoring with Discourse-Aware Neural Models—0
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction—0
Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector—0
Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification—0
Extraction of Heart Rate from PPG Signal: A Machine Learning Approach using Decision Tree Regression Algorithm—0
Extractive Text Summarization using Neural Networks—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified