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 1201–1225 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified