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 326–350 of 1706 papers

TitleStatusHype
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text—0
Bidirectional LSTM for Named Entity Recognition in Twitter Messages—0
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals—0
Bi-LSTM Price Prediction based on Attention Mechanism—0
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs—0
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering—0
Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
Biologically Inspired Oscillating Activation Functions Can Bridge the Performance Gap between Biological and Artificial Neurons—0
Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization—0
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection—0
A Numbers Game: Numeric Encoding Options with Automunge—0
Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification—0
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks—0
A Feature Induction Algorithm with Application to Named Entity Disambiguation—0
Breast mass classification in ultrasound based on Kendall's shape manifold—0
Bridging the Semantic Gap in Virtual Machine Introspection and Forensic Memory Analysis—0
Bringing Structure to Naturalness: On the Naturalness of ASTs—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
Building automated vandalism detection tools for Wikidata—0
Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench—0
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering—0
Bayesian Kernel Methods for Natural Language Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified