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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1076–1100 of 1706 papers

TitleStatusHype
GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text—0
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees—0
Graph-based Dependency Parsing with Bidirectional LSTM—0
Graph Classification via Reference Distribution Learning: Theory and Practice—0
GraphCNNpred: A stock market indices prediction using a Graph based deep learning system—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Graph Neural Networks and Boolean Satisfiability—0
Grasp-and-Lift Detection from EEG Signal Using Convolutional Neural Network—0
Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction—0
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks—0
GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent—0
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores—0
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition—0
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection—0
Hashtag Recommendation with Topical Attention-Based LSTM—0
HCCL at SemEval-2018 Task 8: An End-to-End System for Sequence Labeling from Cybersecurity Reports—0
Helsinki-NLP at SemEval-2022 Task 2: A Feature-Based Approach to Multilingual Idiomaticity Detection—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users—0
Heuristic Feature Selection for Clickbait Detection—0
HiCat: A Semi-Supervised Approach for Cell Type Annotation—0
Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing—0
Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health—0
High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing—0
Highly Effective Arabic Diacritization using Sequence to Sequence Modeling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified