- author = "Dong Song and Malcolm I. Heywood and A. Nur Zincir-Heywood",
- title = "Training genetic programming on half a million patterns: an example from anomaly detection",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2005",
- volume = "9",
- number = "3",
- pages = "225--239",
- month = jun,
- ISSN = "1089-778X",
- keywords = "genetic algorithms, genetic programming, data mining, learning (artificial intelligence), security of data, anomaly detection, genetic programming training, hierarchical RSS-DSS algorithm, hierarchical fitness functions, large dataset dynamical filtering, real-world KDD-99 intrusion detection data set,Dynamic subset selection (DSS), hierarchical cost function, intrusion detection, large data sets",
-
DOI = "
doi:10.1109/TEVC.2004.841683",
- size = "15 pages",
- abstract = "The hierarchical RSS-DSS algorithm is introduced for dynamically filtering large datasets based on the concepts of training pattern age and difficulty, while using a data structure to facilitate the efficient use of memory hierarchies. Such a scheme provides the basis for training genetic programming (GP) on a data set of half a million patterns in 15 min. The method is generic, thus, not specific to a particular GP structure, computing platform, or application context. The method is demonstrated on the real-world KDD-99 intrusion detection data set, resulting in solutions competitive with those identified in the original KDD-99 competition, while only using a fraction of the original features. Parameters of the RSS-DSS algorithm are demonstrated to be effective over a wide range of values. An analysis of different cost functions indicates that hierarchical fitness functions provide the most effective solutions.",
-
notes = "INSPEC Accession Number: 8458415
Quest Software Inc., Halifax, NS, Canada
p226 only uses {"}8 of 41 features{"}. Linear GP, L-GP. p227 {"}No benefits were observed in making such a mutation operator field specific{"} p235 {"}not all training set patterns are equally significant{"}
",
