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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 1101–1150 of 1706 papers

TitleStatusHype
Supervised Typing of Big Graphs using Semantic Embeddings—0
Survey on Embedding Models for Knowledge Graph and its Applications—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
SWDE : A Sub-Word And Document Embedding Based Engine for Clickbait Detection—0
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks—0
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields—0
Syntax Aware LSTM Model for Chinese Semantic Role Labeling—0
Syntax Aware LSTM model for Semantic Role Labeling—0
Syntax Encoding with Application in Authorship Attribution—0
Systematic Literature Review on Application of Machine Learning in Continuous Integration—0
SYSTRAN Participation to the WMT2018 Shared Task on Parallel Corpus Filtering—0
SZTE-NLP: Sentiment Detection on Twitter Messages—0
Tabular Feature Discovery With Reasoning Type Exploration—0
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration—0
Tackling Data Drift with Adversarial Validation: An Application for German Text Complexity Estimation—0
Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning—0
Tackling Sequence to Sequence Mapping Problems with Neural Networks—0
TaDeR: A New Task Dependency Recommendation for Project Management Platform—0
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news—0
Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks—0
Team_BUDDI at ComMA@ICON: Exploring Individual and Joint Modelling Approaches for Detecting Aggression, Communal Bias and Gender Bias—0
Techniques for Automated Machine Learning—0
TEET! Tunisian Dataset for Toxic Speech Detection—0
Temperature Distribution Prediction in Laser Powder Bed Fusion using Transferable and Scalable Graph Neural Networks—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
Temporal Tensor Transformation Network for Multivariate Time Series Prediction—0
Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?—0
Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning—0
Text embedding models can be great data engineers—0
TFBEST: Dual-Aspect Transformer with Learnable Positional Encoding for Failure Prediction—0
The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger—0
The binary trio at SemEval-2019 Task 5: Multitarget Hate Speech Detection in Tweets—0
The Death of Feature Engineering? BERT with Linguistic Features on SQuAD 2.0—0
The Effect of Visual Design in Image Classification—0
The GW/LT3 VarDial 2016 Shared Task System for Dialects and Similar Languages Detection—0
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis—0
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model—0
The impact of simple feature engineering in multilingual medical NER—0
Thelxinoë: Recognizing Human Emotions Using Pupillometry and Machine Learning—0
The Randomized Causation Coefficient—0
The Recent Advances in Automatic Term Extraction: A survey—0
The Spatially-Conscious Machine Learning Model—0
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches—0
This is how we do it: Answer Reranking for Open-domain How Questions with Paragraph Vectors and Minimal Feature Engineering—0
Three-Class Text Sentiment Analysis Based on LSTM—0
Retrieval-Augmented Feature Generation for Domain-Specific Classification—0
Time Series Featurization via Topological Data Analysis—0
Time Series Prediction using Deep Learning Methods in Healthcare—0
Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified