R. Rajakumar1,*, S. Sathiya Devi2
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2101-2116, 2023, DOI:10.32604/iasc.2023.028889
Abstract Due to the advancements in information technologies, massive quantity of data is being produced by social media, smartphones, and sensor devices. The investigation of data stream by the use of machine learning (ML) approaches to address regression, prediction, and classification problems have received considerable interest. At the same time, the detection of anomalies or outliers and feature selection (FS) processes becomes important. This study develops an outlier detection with feature selection technique for streaming data classification, named ODFST-SDC technique. Initially, streaming data is pre-processed in two ways namely categorical encoding and null value removal. In addition, Local Correlation Integral (LOCI)… More >
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