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 1126–1150 of 1706 papers

TitleStatusHype
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