A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems
By D. Micci-Barreca. SKDD, 2001
303 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/f3f3054b04b4554f00a25a698393625c4f784bb5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Aleksandr Vorobev
- Liudmila Ostroumova
- Gleb Gusev
- Vasily Ershov
- Tong Wang
- Anna Veronika Dorogush
- Andrey Gulin
- Amitai Armon
In Information Systems
- Aleksandr Vorobev
- Liudmila Ostroumova
- Gleb Gusev
- Vasily Ershov
- Tong Wang
- Anna Veronika Dorogush
- Andrey Gulin
Citations in the corpus
- CatBoost: unbiased boosting with categorical features (2017, methodology): “The straightforward approach to estimate the expected target is to take its average value over the whole training dataset3 [6, 18]:”
- CatBoost: gradient boosting with categorical features support (2018, methodology): “For binary classification task a prior is usually an a priori probability of encountering a positive class [14].”
- Multi-Value Rule Sets (2017, background): “Micci-Barreca [13] presented a data-preprocessing scheme that transforms high-cardinality categorical attributes into quasi-continuous scalar attributes suited for use in regression-type models.”