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 976–1000 of 1706 papers

TitleStatusHype
Explainable Multi-class Classification of Medical Data—0
Explainable Neural Networks based on Additive Index Models—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
Exploring Adversarial Examples in Malware Detection—0
Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems—0
Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets—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
Extracting Human Temporal Orientation from Facebook Language—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
Fake News Detection using Stance Classification: A Survey—0
Fake News Early Detection: An Interdisciplinary Study—0
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach—0
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
Fast and Accurate Performance Analysis of LTE Radio Access Networks—0
Fast and Accurate Reordering with ITG Transition RNN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified