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 1251–1275 of 1706 papers

TitleStatusHype
Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks—0
THU\_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task LearningCode0
Multi-Scale DenseNet-Based Electricity Theft Detection—0
A Brand-level Ranking System with the Customized Attention-GRU Model—0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach—0
Extended pipeline for content-based feature engineering in music genre recognition—0
Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Reusable workflows for gender prediction—0
Attention for Implicit Discourse Relation Recognition—0
PyRATA, Python Rule-based feAture sTructure AnalysisCode0
RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering—0
DeepTriangle: A Deep Learning Approach to Loss ReservingCode0
Data-Driven Investigative Journalism For Connectas Dataset—0
False Information on Web and Social Media: A SurveyCode0
A machine learning model for identifying cyclic alternating patterns in the sleeping brain—0
Event Extraction with Generative Adversarial Imitation Learning—0
A Deep Representation Empowered Distant Supervision Paradigm for Clinical Information Extraction—0
Learning to Extract Coherent Summary via Deep Reinforcement Learning—0
Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition—0
DeepFM: An End-to-End Wide & Deep Learning Framework for CTR PredictionCode0
A Simple and Effective Approach to the Story Cloze Test—0
PotentialNet for Molecular Property Prediction—0
An Unsupervised Model with Attention Autoencoders for Question Retrieval—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified