Incremental Interval Regression Tree Learning with Mean Variance Numerical Data Streams |
UDC: 005.521 ; 005.82 DOI: 10.7595/management.fon.2012.0013
In this paper, we present a novel method for interval regression tree incremental learning with mean variance patterned numerical data streams. The proposed Mean Variance Interval Regression Tree (MVIRT) algorithm transforms continuous temporal data into two statistical moments according to a user-specified time resolution and builds a regression model tree for estimating the prediction interval of the target variable. The algorithm main properties are time - based incremental mean variance tree induction algorithm accompanying novel time resolution and outliers detection mechanism. Results of real world data stream show that the MVIRT algorithm produces more accurate and easily interpretable prediction models than other state-of-the-art batch incremental model tree methods. Keywords: prediction, regression tree, incremental learning, data stream mining, interval prediction
Dima Alberg Department of Industrial Engineering and Management, SCE - Shamoon College of Engineering, Beer-Sheva, Israel
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