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 901–925 of 1706 papers

TitleStatusHype
On the Replicability and Reproducibility of Deep Learning in Software Engineering—0
AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced LossCode0
Graph Convolutional Neural Networks for analysis of EEG signals, BCI applicationCode0
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks—0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System—0
Semantic Loss Application to Entity Relation Recognition—0
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature—0
A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification—0
Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities—0
Unlocking New York City Crime Insights using Relational Database Embeddings—0
DENS-ECG: A Deep Learning Approach for ECG Signal Delineation—0
Leveraging Affective Bidirectional Transformers for Offensive Language Detection—0
Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms—0
A Deep Learning Approach for Automatic Detection of Fake NewsCode0
Article citation study: Context enhanced citation sentiment detection—0
Scoring Root Necrosis in Cassava Using Semantic Segmentation—0
A neural network model for solvency calculations in life insurance—0
Detecting Troll Tweets in a Bilingual Corpus—0
Effort Estimation in Named Entity Tagging Tasks—0
Affect inTweets: A Transfer Learning Approach—0
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language—0
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
A Kernel Two-sample Test for Dynamical Systems—0
Identifying Semantically Duplicate Questions Using Data Science Approach: A Quora Case Study—0
Template-based Question Answering using Recursive Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified