Open AccessA novel approach using incremental under sampling for data stream mining
Author(s) -
N Anupama,
Sudarson Jena
Publication year - 2017
Publication title -
big data and information analytics
Language(s) - English
Resource type - Journals
eISSN - 2380-6974
pISSN - 2380-6966
DOI - 10.3934/bdia.2017017
Subject(s) - data stream mining , benchmark (surveying) , data stream , computer science , data mining , sampling (signal processing) , streams , precision and recall , tree (set theory) , process (computing) , machine learning , artificial intelligence , mathematics , telecommunications , computer network , mathematical analysis , geodesy , filter (signal processing) , computer vision , geography , operating system
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 nature present 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.