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Feature engineering

Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature… 
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Related topics

Related topics

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Constrained conditional modelDeep feature synthesisDeep learningFeature (machine learning)

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Feature Engineering for Mid-Price Prediction With Deep Learning
Mid-price movement prediction based on the limit order book data is a challenging task due to the complexity and dynamics of the… 
2019
2019
Neural Feature Search: A Neural Architecture for Automated Feature Engineering
Feature engineering is a crucial step for developing effective machine learning models. Traditionally, feature engineering is… 
Highly Cited
2018
Highly Cited
2018
Deep EHR: Chronic Disease Prediction Using Medical Notes
Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient… 
2018
2018
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning
This paper describes the results of NILC team at CWI 2018. We developed solutions following three approaches: (i) a feature… 
Highly Cited
2017
Highly Cited
2017
Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition
We present an unconstrained ear recognition framework that outperforms state-of-the-art systems in different publicly available… 
Highly Cited
2016
Highly Cited
2016
Deep Deformation Network for Object Landmark Localization
We propose a novel cascaded framework, namely deep deformation network (DDN), for localizing landmarks in non-rigid objects. The… 
Review
2016
Review
2016
Space-Time Representation of People Based on 3D Skeletal Data: A Review
Highly Cited
2015
Highly Cited
2015
Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of… 
Highly Cited
2014
Highly Cited
2014
A Neuroevolution Approach to General Atari Game Playing
This paper addresses the challenge of learning to play many different video games with little domain-specific knowledge… 
Highly Cited
2013
Highly Cited
2013
Tandem HMM with convolutional neural network for handwritten word recognition
In this paper, we investigate the combination of hidden Markov models and convolutional neural networks for handwritten word… 
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