Order reprints

A NOVEL APPROACH USING INCREMENTAL UNDER SAMPLING FOR DATA STREAM MINING

Anupama N Sudarson Jena

*Corresponding author: Anupama N anupama.niranjan@gmail.com

BDIA2018,1,1doi:10.3934/bdia.2017017

Data stream mining is every popular in recent years with advanced electronic devices generating continuous data streams. The performance of standard learning algorithms has been compromised with imbalance naturepresent in real world data streams. In this paper, we propose an algorithm known as Increment Under Sampling for Data streams (IUSDS) which uses an unique under sampling technique to almost balance the data sets to minimize the effect of imbalance in stream mining process. The experimental analysis conducted suggests that the proposed algorithm improves the knowledge discovery over benchmark algorithms like C4.5 and Hoeffding tree in terms of standard performance measures namely accuracy, AUC, precision, recall, F-measure, TP rate, FP rate and TN rate.

Please supply your name and a valid email address you yourself

Fields marked*are required

Article URL   http://www.aimspress.com/BDIA/article/3032.html
Article ID   2380-6966_2018_1_1
Editorial Email  
Your Name *
Your Email *
Quantity *

Copyright © AIMS Press All Rights Reserved