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 351–400 of 1706 papers

TitleStatusHype
Automatic deductive coding in discourse analysis: an application of large language models in learning analyticsCode0
Automatic Argumentative-Zoning Using Word2vecCode0
Identifying Quantum Phase Transitions with Adversarial Neural NetworksCode0
IIFE: Interaction Information Based Automated Feature EngineeringCode0
Deep Learning-Based Automatic Downbeat Tracking: A Brief ReviewCode0
Deep Learning for Answer Sentence SelectionCode0
Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health ClassificationCode0
A Position-aware Bidirectional Attention Network for Aspect-level Sentiment AnalysisCode0
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained EnvironmentsCode0
Automated Treatment Planning in Radiation Therapy using Generative Adversarial NetworksCode0
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
Interpretation of Semantic Tweet RepresentationsCode0
An Empirical Study on the Usage of Automated Machine Learning ToolsCode0
CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side InformationCode0
Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature AugmentationCode0
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?Code0
DeepInf: Social Influence Prediction with Deep LearningCode0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
CharNER: Character-Level Named Entity RecognitionCode0
LAC : LSTM AUTOENCODER with Community for Insider Threat DetectionCode0
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR ModelsCode0
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A SurveyCode0
Learning Sentiment-Specific Word Embedding for Twitter Sentiment ClassificationCode0
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR PredictionCode0
A Deep Learning Approach for Automatic Detection of Fake NewsCode0
Leveraging Latents for Efficient Thermography Classification and SegmentationCode0
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral FeaturesCode0
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in textCode0
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient DetectionCode0
Machine learning and chord based feature engineering for genre prediction in popular Brazilian musicCode0
Applying Deep Learning to Basketball TrajectoriesCode0
Machine Learning-Based Completions Sequencing for Well Performance OptimizationCode0
Deep Affix Features Improve Neural Named Entity RecognizersCode0
An Empirical Analysis of Feature Engineering for Predictive ModelingCode0
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
Match-Tensor: a Deep Relevance Model for SearchCode0
DeepAtom: A Framework for Protein-Ligand Binding Affinity PredictionCode0
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous SensorsCode0
DDGK: Learning Graph Representations for Deep Divergence Graph KernelsCode0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
Deduplication Over Heterogeneous Attribute Types (D-HAT)Code0
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph ClassificationCode0
MONAH: Multi-Modal Narratives for Humans to analyze conversationsCode0
Clickbait Detection in Tweets Using Self-attentive NetworkCode0
Multiple perspectives HMM-based feature engineering for credit card fraud detectionCode0
My tweets bring all the traits to the yard: Predicting personality and relational traits in Online Social NetworksCode0
AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced LossCode0
Named Entity Recognition with Bidirectional LSTM-CNNsCode0
DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction PredictionCode0
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait RecognitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified