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 581–590 of 1706 papers

TitleStatusHype
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition—0
A Sentence Interaction Network for Modeling Dependence between Sentences—0
A Linear Baseline Classifier for Cross-Lingual Pronoun Prediction—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Artificial Neural Networks for Finger Vein Recognition: A Survey—0
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language—0
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
Artificial Intelligence Technology analysis using Artificial Intelligence patent through Deep Learning model and vector space model—0
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified