Discriminant Analysis on a Stream of Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00357775" target="_blank" >RIV/68407700:21240/22:00357775 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Discriminant Analysis on a Stream of Features

Popis výsledku v původním jazyce

Online learning is a well-established problem in machine learning. But while online learning is commonly concerned with learning on a stream of samples, this article is concerned with learning on a stream on features. An online quadratic discriminant analysis (QDA) is proposed because it is fast, capable of modeling feature interactions, and it can still return an exact solution. When a new feature is inserted into a training set, the proposed implementation of QDA showed a 1000-fold speed up to scikit-learn QDA. Fast learning on a stream of features provides a data scientist with timely feedback about the importance of new features during the feature engineering phase. In the production phase, it reduces the cost of updating a model when a new source of potentially useful features appears.

Název v anglickém jazyce

Discriminant Analysis on a Stream of Features

Popis výsledku anglicky

Online learning is a well-established problem in machine learning. But while online learning is commonly concerned with learning on a stream of samples, this article is concerned with learning on a stream on features. An online quadratic discriminant analysis (QDA) is proposed because it is fast, capable of modeling feature interactions, and it can still return an exact solution. When a new feature is inserted into a training set, the proposed implementation of QDA showed a 1000-fold speed up to scikit-learn QDA. Fast learning on a stream of features provides a data scientist with timely feedback about the importance of new features during the feature engineering phase. In the production phase, it reduces the cost of updating a model when a new source of potentially useful features appears.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Engineering Applications of Neural Networks

ISBN

978-3-031-08222-1

ISSN

1865-0929

e-ISSN

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Počet stran výsledku

12

Strana od-do

223-234

Název nakladatele

Springer, Cham

Místo vydání

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Místo konání akce

Limenas Hersonissou 700 14 Crete

Datum konání akce

17. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000926169100019