A novel approach using incremental multi modal oversampling for data stream mining

ANUPAMA N; RAVI SANKAR V; SUDARSON JENA

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A novel approach using incremental multi modal oversampling for data stream mining

Author(s)

ANUPAMA N DGitam School of Technology, Department of CSE, Hyderabad, Telangana, India

RAVI SANKAR V Gitam School of Technology, Department of CSE,Hyderabad, Telangana, India

SUDARSON JENA SUIIT, Department of Computer Science Engineering & Application,Sambalpur, Odissa, India

Abstract

Data mining is the process of discovering hidden knowledge from the existing datasets. The process of knowledge discovery is a complex task when the data source is in the form of data streams and more tough when the data source is of class imbalance in nature. To find an optimal solution for these problems many research proposals are formulated by researchers. Some of the unsolved problems in the literature for the above said problem are for very large data sources of data streams with class imbalance nature. In this paper, a novel proposal for class imbalance large data streams is presented with novel techniques of oversampling and a unique multi modal filtering technique known as Multimodal Increment over Sampling for Data Streams (MIOSDS). The experimental simulations are conducted on three large datasets with different domains with high class imbalance ratio. The results generated are very impressive in terms of accuracy, AUC, precision, recall and F-measure validation metrics.

Publication Details

Page(s) 5675-5687

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 19, Year: 2022

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Keywords

Knowledge discovery, Imbalanced data, Multimodal Increment Over Sampling for Data Streams (MIOSDS), Data Streams, oversampling

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