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 1551–1575 of 1706 papers

TitleStatusHype
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches—0
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT—0
Combining Lexical and Semantic-based Features for Answer Sentence Selection—0
Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures—0
Combining Tree Structures, Flat Features and Patterns for Biomedical Relation Extraction—0
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection—0
Comparative Analysis of Machine Learning and Deep Learning Algorithms for Detection of Online Hate Speech—0
Comparative Performance of Machine Learning Algorithms for Early Genetic Disorder and Subclass Classification—0
Introducing 3DCNN ResNets for ASD full-body kinematic assessment: a comparison with hand-crafted features—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Comparing fingers and gestures for bci control using an optimized classical machine learning decoder—0
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language—0
Comparing Word Representations for Implicit Discourse Relation Classification—0
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition—0
Complex Word Identification: Convolutional Neural Network vs. Feature Engineering—0
Computational Models for Academic Performance Estimation—0
Computing committor functions for the study of rare events using deep learning with importance sampling—0
Computing Committor Functions for the Study of Rare Events Using Deep Learning—0
Concepts for Automated Machine Learning in Smart Grid Applications—0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data—0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation—0
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified