Unsupervised generative feature transformation via graph contrastive pre-training and multi-objective fine-tuning
Feature transformation is to derive a new feature set from original features to augment the AI
power of data. In many science domains such as material performance screening, while …
power of data. In many science domains such as material performance screening, while …
Data is the new oil–sort of: a view on why this comparison is misleading and its implications for modern data administration
C Stach - Future Internet, 2023 - mdpi.com
Currently, data are often referred to as the oil of the 21st century. This comparison is not only
used to express that the resource data are just as important for the fourth industrial …
used to express that the resource data are just as important for the fourth industrial …
Relational data embeddings for feature enrichment with background information
For many machine-learning tasks, augmenting the data table at hand with features built from
external sources is key to improving performance. For instance, estimating housing prices …
external sources is key to improving performance. For instance, estimating housing prices …
OpenFE: automated feature generation with expert-level performance
The goal of automated feature generation is to liberate machine learning experts from the
laborious task of manual feature generation, which is crucial for improving the learning …
laborious task of manual feature generation, which is crucial for improving the learning …
“It's Like the Value System in the Loop”: Domain Experts' Values Expectations for NLP Automation
D Showkat, EPS Baumer - Proceedings of the 2022 ACM Designing …, 2022 - dl.acm.org
The rise of automated text processing systems has led to the development of tools designed
for a wide variety of application domains. These technologies are often developed to support …
for a wide variety of application domains. These technologies are often developed to support …
Catch: Collaborative feature set search for automated feature engineering
Feature engineering often plays a crucial role in building mining systems for tabular data,
which traditionally requires experienced human experts to perform. Thanks to the rapid …
which traditionally requires experienced human experts to perform. Thanks to the rapid …
[PDF][PDF] SemFORMS: Automatic Generation of Semantic Transforms By Mining Data Science Code.
Careful choice of feature transformations in a dataset can help predictive model
performance, data understanding and data exploration. However, finding useful features is a …
performance, data understanding and data exploration. However, finding useful features is a …
Openfe: Automated feature generation beyond expert-level performance
The goal of automated feature generation is to liberate machine learning experts from the
laborious task of manual feature generation, which is crucial for improving the learning …
laborious task of manual feature generation, which is crucial for improving the learning …
A comparison of decision forest inference platforms from a database perspective
H Guan, MR Dwarampudi, V Gunda, H Min… - arxiv preprint arxiv …, 2023 - arxiv.org
Decision forest, including RandomForest, XGBoost, and LightGBM, is one of the most
popular machine learning techniques used in many industrial scenarios, such as credit card …
popular machine learning techniques used in many industrial scenarios, such as credit card …
Empowering Machine Learning with Scalable Feature Engineering and Interpretable AutoML
Automated feature engineering has gained considerable attention in academia and industry.
Nevertheless, existing systems often lack practical scalability and efficiency. This paper …
Nevertheless, existing systems often lack practical scalability and efficiency. This paper …