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 551–575 of 1706 papers

TitleStatusHype
A Deep Neural Network Approach To Parallel Sentence Extraction—0
Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation—0
A Brand-level Ranking System with the Customized Attention-GRU Model—0
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation—0
Country-level Arabic Dialect Identification using RNNs with and without Linguistic Features—0
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks—0
Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading—0
Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation—0
A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects—0
Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking—0
ASM Kernel: Graph Kernel using Approximate Subgraph Matching for Relation Extraction—0
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data—0
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora—0
A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting—0
Alzheimer's Disease Detection from Spontaneous Speech and Text: A review—0
A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams—0
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection—0
Content Selection for Real-time Sports News Construction from Commentary Texts—0
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization—0
Concepts for Automated Machine Learning in Smart Grid Applications—0
Computing Committor Functions for the Study of Rare Events Using Deep Learning—0
A simple framework for contrastive learning phases of matter—0
Computing committor functions for the study of rare events using deep learning with importance sampling—0
Computational Models for Academic Performance Estimation—0
A Simple and Effective Dependency Parser for Telugu—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified