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Feature Engineering

145 papers with code · Methodology

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.

Benchmarks

Latest papers with code

Transfer Learning for Brain-Computer Interfaces: A Complete Pipeline

3 Jul 2020drwuHUST/TLBCI

Transfer learning (TL) has been widely used in electroencephalogram (EEG) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance.

EEG FEATURE ENGINEERING TRANSFER LEARNING

9
03 Jul 2020

Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation

6 Jun 2020DavideBuffelli/TrASenD

We propose a simple and effective transfer-learning based strategy to adapt a model to a specific user, providing an average increment of $6\%$ on the F1 score on the predictions for that user.

ACTIVITY RECOGNITION FEATURE ENGINEERING TIME SERIES TRANSFER LEARNING

2
06 Jun 2020

General-Purpose User Embeddings based on Mobile App Usage

27 May 2020Junqi-Zhang/AETN

In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage.

FEATURE ENGINEERING

8
27 May 2020

A Survey of Information Cascade Analysis: Models, Predictions and Recent Advances

22 May 2020Xovee/casflow

The deluge of digital information in our daily life -- from user-generated content such as microblogs and scientific papers, to online business such as viral marketing and advertising -- offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades.

FEATURE ENGINEERING

5
22 May 2020

CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

20 Apr 2020mmcdermott/chexpertplusplus

The extraction of labels from radiology text reports enables large-scale training of medical imaging models.

FEATURE ENGINEERING

3
20 Apr 2020

SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction

7 Mar 2020jingpeilu/psmnet_ros

Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue.

FEATURE ENGINEERING POSE ESTIMATION TRANSFER LEARNING

2
07 Mar 2020

Supervised Learning on Relational Databases with Graph Neural Networks

6 Feb 2020mwcvitkovic/Supervised-Learning-on-Relational-Databases-with-GNNs

The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.].

FEATURE ENGINEERING

17
06 Feb 2020

Knowledge-aware Attention Network for Protein-Protein Interaction Extraction

7 Jan 2020zhuango/KAN

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts.

FEATURE ENGINEERING

7
07 Jan 2020

Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification

4 Jan 2020AlexMRuch/Can-x2vec-Save-Lives

Visualizing graph embeddings annotated with predictions of potentially suicidal individuals shows the integrated model could classify such individuals even if they are positioned far from the support group.

ACTION CLASSIFICATION ACTIVITY PREDICTION DOCUMENT EMBEDDING FEATURE ENGINEERING GRAPH EMBEDDING

6
04 Jan 2020

See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Data

3 Dec 2019paulomann/ReadOrSee

However, nowadays, the data shared at social media is a ubiquitous source that can be used to detect the depression symptoms even when the student is not able to afford or search for professional care.

 Ranked #1 on Feature Engineering on 2019_test set (using extra training data)

FEATURE ENGINEERING

2
03 Dec 2019