Evolution of Space-Partitioning Forest for Anomaly Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Zhao:2017:GPTP,

• author = "Zhiruo Zhao and Stuart W. Card and Kishan G. Mehrotra and Chilukuri K. Mohan",
• title = "Evolution of Space-Partitioning Forest for Anomaly Detection",
• booktitle = "Genetic Programming Theory and Practice XV",
• editor = "Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo",
• year = "2017",
• series = "Genetic and Evolutionary Computation",
• pages = "169--184",
• address = "University of Michigan in Ann Arbor, USA",
• month = may # " 18--20",
• organisation = "the Center for the Study of Complex Systems",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-90511-2",
• URL = "https://link.springer.com/chapter/10.1007/978-3-319-90512-9_11",
• DOI = "doi:10.1007/978-3-319-90512-9_11",
• abstract = "Previous work proposed a fast one-class anomaly detector using an ensemble of random half-space partitioning trees. The method was shown to be effective and efficient for detecting anomalies in streaming data. However, the parameters were pre-defined, so the random partitions of the data space might not be optimal. Therefore, the aims of this study were to: (a) give some mathematical analysis of the random partitioning trees; and (b) explore optimizing forests for anomaly detection using evolutionary algorithms.",
• notes = "GPTP 2017, Part of \cite{Banzhaf:2017:GPTP} published after the workshop in 2018",

}

Genetic Programming entries for Zhiruo Zhao Stu Card Kishan G Mehrotra Chilukuri K Mohan

Citations