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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 526–550 of 1706 papers

TitleStatusHype
Automatic Prosody Prediction for Chinese Speech Synthesis using BLSTM-RNN and Embedding Features—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
An Error Analysis Tool for Natural Language Processing and Applied Machine Learning—0
Automatic Inference of the Tense of Chinese Events Using Implicit Linguistic Information—0
Adversarial training for tabular data with attack propagation—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
An entity-driven recursive neural network model for chinese discourse coherence modeling—0
Automatic Features for Essay Scoring -- An Empirical Study—0
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring—0
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion—0
Automatic Feature Engineering for Answer Selection and Extraction—0
An Enhanced Ad Event-Prediction Method Based on Feature Engineering—0
Aalto's End-to-End DNN systems for the INTERSPEECH 2020 Computational Paralinguistics Challenge—0
Downsampling and geometric feature methods for EEG classification tasks with CNNs—0
Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks—0
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
An End-to-End Graph Convolutional Kernel Support Vector Machine—0
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence—0
Automatic Analysis of Linguistic Features in Journal Articles of Different Academic Impacts with Feature Engineering Techniques—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
Streaming Adaptive Nonparametric Variational Autoencoder—0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified