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 576–600 of 1706 papers

TitleStatusHype
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Automatic Features for Essay Scoring -- An Empirical Study—0
Downsampling and geometric feature methods for EEG classification tasks with CNNs—0
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling—0
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation—0
An Empirical Study of Discriminative Sequence Labeling Models for Vietnamese Text Processing—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
DROCC: Deep Robust One-Class Classification—0
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Drug-drug Interaction Extraction via Recurrent Neural Network with Multiple Attention Layers—0
Effective Representations of Clinical Notes—0
Dual Training and Dual Prediction for Polarity Classification—0
DUBLIN -- Document Understanding By Language-Image Network—0
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles—0
DUTIR in BioNLP-ST 2016: Utilizing Convolutional Network and Distributed Representation to Extract Complicate Relations—0
Dynamic Adaptation in Data Storage: Real-Time Machine Learning for Enhanced Prefetching—0
Dynamic and Adaptive Feature Generation with LLM—0
Dynamic Facial Analysis: From Bayesian Filtering to Recurrent Neural Network—0
Dynamic Feature Induction: The Last Gist to the State-of-the-Art—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified