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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1250 of 1706 papers

TitleStatusHype
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning—0
Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction—0
Learn2Aggregate: Supervised Generation of Chvátal-Gomory Cuts Using Graph Neural Networks—0
Learnable Wavelet Packet Transform for Data-Adapted Spectrograms—0
Learned Feature Importance Scores for Automated Feature Engineering—0
Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks—0
Learning Adaptable Patterns for Passage Reranking—0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
Learning based Methods for Code Runtime Complexity Prediction—0
Learning behavioral context recognition with multi-stream temporal convolutional networks—0
Learning Concept Taxonomies from Multi-modal Data—0
Learning Connective-based Word Representations for Implicit Discourse Relation Identification—0
Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information—0
Learning Contextually Informed Representations for Linear-Time Discourse Parsing—0
Learning Feature Engineering for Classification—0
Learning Feature Representations for Keyphrase Extraction—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Learning from the Experience of Doctors: Automated Diagnosis of Appendicitis Based on Clinical Notes—0
Learning latent representations for operational nitrogen response rate prediction—0
Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text—0
Learning Non-linear Features for Machine Translation Using Gradient Boosting Machines—0
Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition—0
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction—0
Learning post-processing for QRS detection using Recurrent Neural Network—0
Learning Representations from Road Network for End-to-End Urban Growth Simulation—0
Learning Semantic Textual Similarity with Structural Representations—0
Learning Stylometric Representations for Authorship Analysis—0
Learning Summary Prior Representation for Extractive Summarization—0
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification—0
Learning to Extract Coherent Summary via Deep Reinforcement Learning—0
Learning to Focus when Ranking Answers—0
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models—0
Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation—0
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems—0
Leveraging Affective Bidirectional Transformers for Offensive Language Detection—0
Leveraging Contextual Information for Effective Entity Salience Detection—0
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading—0
Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases—0
Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time—0
Leveraging Latent Representations of Speech for Indian Language Identification—0
Leveraging Machine Learning for Early Autism Detection via INDT-ASD Indian Database—0
Leveraging Open-Source Large Language Models for Native Language Identification—0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Leveraging sinusoidal representation networks to predict fMRI signals from EEG—0
Lexical Bias In Essay Level Prediction—0
LFG-based Features for Noun Number and Article Grammatical Errors—0
LiDAR-based Outdoor Crowd Management for Smart Campus on the Edge—0
LightGBM robust optimization algorithm based on topological data analysis—0
LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification—0
Lightweight Spatio-Temporal Attention Network with Graph Embedding and Rotational Position Encoding for Traffic Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified