Big Data Classification using Evolutionary techniques: A Survey

SEEE DIGIBOOK ON ENGINEERING & TECHNOLOGY, VOL. 03, JUNE 2021 PP. (75-79)
Abstract– The volume of data communicated over the internet is increasing continuously and consistently. The ability to automatically extract useful information from vast amounts of data has become a major concern for organisations with massive datasets. It is possible to gain useful information from the feelings expressed in the message in order to mitigate future hazards. Big Data is generally characterised by three characteristics: velocity, volume, and variety. On the basis of these characteristics, data can be classified in three distinct ways: supervised, unsupervised, and semi-supervised. Recently, a variety of algorithmic and methodological ideas have been made concerning the clustering and classification of data and electronic documents. In this study, we will investigate and analyse various evolutionary algorithms used for the classification of massive data sets.
Index Terms – Some terminology connected with artificial intelligence include big data, genetic algorithm, clustering, neural networks, swarm intelligence, co-evolutionary programming, Naive bayes, and decision trees.
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Srikanth.R, Arun, Manoj, Manopriya, Narmadha
Department of Computer Science and Engineering
Rathinam Technical Campus,
Coimbatore, Tamilnadu, India

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