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 1301–1325 of 1706 papers

TitleStatusHype
Modelling customer churn for the retail industry in a deep learning based sequential framework—0
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network—0
Morphing-based Compression for Data-centric ML Pipelines—0
Morphological Disambiguation from Stemming Data—0
Morphological Profiling for Drug Discovery in the Era of Deep Learning—0
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks—0
MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers—0
MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction—0
Multi-Layer Perceptron Neural Network for Improving Detection Performance of Malicious Phishing URLs Without Affecting Other Attack Types Classification—0
Multi-level Translation Quality Prediction with QuEst++—0
Multilingual and Multitarget Hate Speech Detection in Tweets—0
Multilingual discriminative lexicalized phrase structure parsing—0
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction—0
Multi-Modal Video Feature Extraction for Popularity Prediction—0
Multi-output Headed Ensembles for Product Item Classification—0
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis—0
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection—0
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks—0
Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase—0
Multi-Scale DenseNet-Based Electricity Theft Detection—0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling—0
Multi-Task Bidirectional Transformer Representations for Irony Detection—0
Multi-Task Cross-Lingual Sequence Tagging from Scratch—0
Multitask Learning with Deep Neural Networks for Community Question Answering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified