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 901–925 of 1706 papers

TitleStatusHype
Dynamic Feature Induction: The Last Gist to the State-of-the-Art—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing—0
Early Churn Prediction from Large Scale User-Product Interaction Time Series—0
Early Detection of Myocardial Infarction in Low-Quality Echocardiography—0
Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers—0
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity—0
ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter—0
ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets—0
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification—0
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain—0
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection—0
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models—0
ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods—0
Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition—0
EEG Based Emotion Sensing using convolutional neural networks—0
EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Effective Representations of Clinical Notes—0
Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning—0
Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering—0
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning—0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
Effort Estimation in Named Entity Tagging Tasks—0
Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified