Learning on a Stream of Features with Random Forest
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F19%3A00333704" target="_blank" >RIV/68407700:21240/19:00333704 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Learning on a Stream of Features with Random Forest
Original language description
We study an interesting and challenging problem, supervised learning on a stream of features, in which the size of the feature set is unknown, and not all features are available for learning while leaving the number of observations constant. In this problem, the features arrive one at a time, and the learner’s task is to train a model equivalent to a model trained from "scratch". When a new feature is inserted into the training set, a new set of trees is trained and added into the current forest. However, it is desirable to correct the selection bias: older features has more opportunities to get selected into trees than the new features. We combat the selection bias by adjusting the feature selection distribution. However, while this correction works well, it may require training of many new trees. In order to keep the count of the new trees small, we furthermore put more weight on more recent trees than on the old trees.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 19th Conference Information Technologies - Applications and Theory (ITAT 2019)
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
5
Pages from-to
79-83
Publisher name
CEUR Workshop Proceedings
Place of publication
Aachen
Event location
Donovaly
Event date
Sep 20, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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