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 1051–1075 of 1706 papers

TitleStatusHype
Fake News Detection using Stance Classification: A Survey—0
Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures—0
Image Retrieval and Pattern Spotting using Siamese Neural Network—0
Low-resource Deep Entity Resolution with Transfer and Active Learning—0
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish—0
Computing Committor Functions for the Study of Rare Events Using Deep Learning—0
Deep Learning-Based Automatic Downbeat Tracking: A Brief ReviewCode0
Streaming Adaptive Nonparametric Variational Autoencoder—0
Automatic Health Problem Detection from Gait Videos Using Deep Neural NetworksCode0
Beyond Context: A New Perspective for Word Embeddings—0
CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering—0
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder—0
The binary trio at SemEval-2019 Task 5: Multitarget Hate Speech Detection in Tweets—0
Highly Effective Arabic Diacritization using Sequence to Sequence Modeling—0
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow—0
Incorporating Word Attention into Character-Based Word SegmentationCode0
Breast mass classification in ultrasound based on Kendall's shape manifold—0
Approximation Ratios of Graph Neural Networks for Combinatorial Problems—0
Multiple perspectives HMM-based feature engineering for credit card fraud detectionCode0
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender SystemsCode0
Feature Selection and Feature Extraction in Pattern Analysis: A Literature ReviewCode0
Computing committor functions for the study of rare events using deep learning with importance sampling—0
Selectivity Estimation for Range Predicates using Lightweight Models—0
Fake News Early Detection: An Interdisciplinary Study—0
Latent Variable Session-Based Recommendation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified