@INPROCEEDINGS{boracchi10IJCNN, 
author={Alippi, C. and Boracchi, G. and Roveri, M.}, 
booktitle={Neural Networks (IJCNN), The 2010 International Joint Conference on}, title={Change detection tests using the ICI rule}, 
year={2010}, 
month=july, 
volume={}, 
number={}, 
pages={1 -7}, 
abstract={Designing tests able to effectively detect changes in the stationarity of a process generating data is a challenging problem, in particular when the process is unknown, and the only information available has to be extracted from a set of observations. This work proposes a novel approach for detecting changes in a process generating data whose distribution is unknown. Peculiarity of the approach is the use of the Intersection of Confidence Intervals (ICI) rule to monitor the process evolution. A change detection test derived from this approach is also presented. Experimental results show that the proposed test outperforms state-of-the art solutions, both in terms of efficiency and effectiveness, in particular when a reduced test configuration set is available.}, 
keywords={ICI rule;change detection tests;intersection of confidence intervals;process evolution;process generating data;reliable systems;data analysis;program testing;software reliability;}, 
doi={10.1109/IJCNN.2010.5596537}, 
ISSN={1098-7576},}