TY - GEN
T1 - High-dimensional surveillance
AU - Dávila, Saylisse
AU - Runger, George
AU - Tuv, Eugene
PY - 2011/6/24
Y1 - 2011/6/24
N2 - Many systems (manufacturing, environmental, health, etc.) generate counts (or rates) of events that are monitored to detect changes. Modern data complements event counts with many additional measurements (such as geographic, demographic, and others) that comprise high-dimensional attributes. This leads to an important challenge to detect a change that only occurs within a region, initially unspecified, defined by these attributes and current methods to handle the attribute information are challenged by high-dimensional data. Our approach transforms the problem to supervised learning, so that properties of an appropriate learner can be described. Rather than error rates, we generate a signal (of a system change) from an appropriate feature selection algorithm. A measure of statistical significance is included to control false alarms. Results on simulated examples are provided.
AB - Many systems (manufacturing, environmental, health, etc.) generate counts (or rates) of events that are monitored to detect changes. Modern data complements event counts with many additional measurements (such as geographic, demographic, and others) that comprise high-dimensional attributes. This leads to an important challenge to detect a change that only occurs within a region, initially unspecified, defined by these attributes and current methods to handle the attribute information are challenged by high-dimensional data. Our approach transforms the problem to supervised learning, so that properties of an appropriate learner can be described. Rather than error rates, we generate a signal (of a system change) from an appropriate feature selection algorithm. A measure of statistical significance is included to control false alarms. Results on simulated examples are provided.
KW - Feature selection
KW - process control
KW - tree ensembles
UR - http://www.scopus.com/inward/record.url?scp=79959360993&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959360993&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21738-8_32
DO - 10.1007/978-3-642-21738-8_32
M3 - Conference contribution
AN - SCOPUS:79959360993
SN - 9783642217371
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 245
EP - 252
BT - Artificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings
T2 - 21st International Conference on Artificial Neural Networks, ICANN 2011
Y2 - 14 June 2011 through 17 June 2011
ER -