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.
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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.
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.
In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage.
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.
The extraction of labels from radiology text reports enables large-scale training of medical imaging models.
Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue.
The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.].
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts.
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.
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)