Classification and Novel Class Detection in Data Streams Using Strings. Singh, R. & Chandak, M. B. Open Access Library Journal, 2(5):1–8, May, 2015. Number: 5 Publisher: Scientific Research Publishing![Classification and Novel Class Detection in Data Streams Using Strings [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Data streams are continuous and always keep evolving in nature. Because of these reasons it becomes difficult to handle such data with simple and static strategies. Data stream poses four main challenges to researchers. These are infinite length, concept-evolution, concept-drift and feature evolution. Infinite-length is because of the amount of data having no bounds. Concept-drift is due to slow changes in the concept of stream. Concept-evolution occurs due to presence of unknown classes in data. Feature-evolution is because of new features continuously keeping appearing in the stream and older ones start disappearing. For performing any analysis on such data we first need to convert it into some knowledgeable form and also need to handle the above mentioned challenges. Various strategies have been proposed to tackle these difficulties. But most of them focus on handling the problem of infinite-length and concept-drift. In this paper, we make efforts to propose a string based strategy to handle infinite-length, concept-evolution and concept-drift.
@article{singh_classification_2015,
title = {Classification and {Novel} {Class} {Detection} in {Data} {Streams} {Using} {Strings}},
volume = {2},
copyright = {http://creativecommons.org/licenses/by/4.0/},
url = {http://www.scirp.org/Journal/Paperabs.aspx?paperid=68406},
doi = {10.4236/oalib.1101507},
abstract = {Data streams are continuous and always keep evolving in nature. Because of these reasons it becomes difficult to handle such data with simple and static strategies. Data stream poses four main challenges to researchers. These are infinite length, concept-evolution, concept-drift and feature evolution. Infinite-length is because of the amount of data having no bounds. Concept-drift is due to slow changes in the concept of stream. Concept-evolution occurs due to presence of unknown classes in data. Feature-evolution is because of new features continuously keeping appearing in the stream and older ones start disappearing. For performing any analysis on such data we first need to convert it into some knowledgeable form and also need to handle the above mentioned challenges. Various strategies have been proposed to tackle these difficulties. But most of them focus on handling the problem of infinite-length and concept-drift. In this paper, we make efforts to propose a string based strategy to handle infinite-length, concept-evolution and concept-drift.},
language = {en},
number = {5},
urldate = {2022-03-31},
journal = {Open Access Library Journal},
author = {Singh, Rimjhim and Chandak, Manoj B.},
month = may,
year = {2015},
note = {Number: 5
Publisher: Scientific Research Publishing},
pages = {1--8},
}
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