About: PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams
Creator	Koh, Yun Dobbie, Gillian Lacombe, Thomas Wu, Ocean
Source	PMC
abstract	In order to adapt random forests to the dynamic nature of data streams, the state-of-the-art technique discards trained trees and grows new trees when concept drifts are detected. This is particularly wasteful when recurrent patterns exist. In this work, we introduce a novel framework called PEARL, which uses both an exact technique and a probabilistic graphical model with Lossy Counting, to replace drifted trees with relevant trees built from the past. The exact technique utilizes pattern matching to find the set of drifted trees, that co-occurred in predictions in the past. Meanwhile, a probabilistic graphical model is being built to capture the tree replacements among recurrent concept drifts. Once the graphical model becomes stable, it replaces the exact technique and finds relevant trees in a probabilistic fashion. Further, Lossy Counting is applied to the graphical model which brings an added theoretical guarantee for both error rate and space complexity. We empirically show our technique outperforms baselines in terms of cumulative accuracy on both synthetic and real-world datasets.
has issue date	2020-04-17(xsd:dateTime)
bibo:doi	10.1007/978-3-030-47436-2_2
has license	no-cc
sha1sum (hex)	20488171771062895127afbb276541247cf2b0e0
schema:url	https://doi.org/10.1007/978-3-030-47436-2_2
resource representing a document's title	PEARL: Probabilistic Exact Adaptive Random Forest with Lossy Counting for Data Streams
has PubMed Central identifier	PMC7206241
schema:publication	Advances in Knowledge Discovery and Data Mining
resource representing a document's body	covid:20488171771062895127afbb276541247cf2b0e0#body_text
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