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 1176–1200 of 1706 papers

TitleStatusHype
IOA: Improving SVM Based Sentiment Classification Through Post Processing—0
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support—0
IoT Device Identification Based on Network Communication Analysis Using Deep Learning—0
IoT Device Identification Using Deep Learning—0
IoT Security: Botnet detection in IoT using Machine learning—0
Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset—0
Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate—0
ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing—0
Joint Feature Selection in Distributed Stochastic Learning for Large-Scale Discriminative Training in SMT—0
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution—0
KeLP: a Kernel-based Learning Platform for Natural Language Processing—0
Keyphrase Extraction with Span-based Feature Representations—0
Keyword spotting -- Detecting commands in speech using deep learning—0
Knowledge-driven Site Selection via Urban Knowledge Graph—0
Lagged correlation-based deep learning for directional trend change prediction in financial time series—0
Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network—0
Language Semantics Interpretation with an Interaction-based Recurrent Neural Networks—0
Large Language Models for Networking: Workflow, Advances and Challenges—0
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level—0
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features—0
Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models—0
Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques—0
Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters—0
Latent Variable Session-Based Recommendation—0
Lateral Movement Detection Using User Behavioral Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified