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 726–750 of 1706 papers

TitleStatusHype
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver—0
Unsupervised Continual Learning in Streaming Environments—0
Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation—0
A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the OvenCode0
RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce—0
Precog-LTRC-IIITH at GermEval 2021: Ensembling Pre-Trained Language Models with Feature EngineeringCode0
Personality Trait Identification Using the Russian Feature Extraction Toolkit—0
Time Series Prediction using Deep Learning Methods in Healthcare—0
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
Towards Personalized and Human-in-the-Loop Document Summarization—0
Data-driven Smart Ponzi Scheme Detection—0
Feature Engineering with Regularity StructuresCode0
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell—0
Deep Learning Chromatic and Clique Numbers of GraphsCode0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Classification of Electrical Impedance Tomography Data Using Machine Learning—0
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning—0
Alejandro Mosquera at SemEval-2021 Task 1: Exploring Sentence and Word Features for Lexical Complexity Prediction—0
CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way—0
A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified