- author = "Khaled Badran and Alaa Rohim",
- title = "Genetic Programming Feature Extraction with Different Robust Classifiers for Network Intrusion Detection",
- journal = "International Journal of Computer Applications",
- year = "2017",
- volume = "168",
- number = "1",
- pages = "37--43",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Pattern Recognition, Classification, Network Intrusion, Feature Extraction, Neural Network, ANN, Support Vector Machines, SVM, Decision Trees",
- publisher = "Foundation of Computer Science (FCS), NY, USA",
- ISSN = "0975-8887",
-
URL = "
https://www.ijcaonline.org/archives/volume168/number1/27841-2017914276",
-
URL = "
https://www.ijcaonline.org/archives/volume168/number1/badran-2017-ijca-914276.pdf",
-
DOI = "
doi:10.5120/ijca2017914276",
- size = "7 pages",
- abstract = "we compare the performance of three traditional robust classifiers (Neural Networks, Support Vector Machines, and Decision Trees) with and without using multi-objective genetic programming in the feature extraction phase. We argue that effective feature extraction can significantly enhance the performance of these classifiers. We have applied these three classifiers stand alone to real world five datasets from the UCI machine learning database and also to network intrusion KDD-99 cup dataset.Then,the experiments were repeated by adding the feature extraction phase.Theresults ofthetwo approachesare compared and conclude that the effective method is to evolve optimal feature extractors that transform input pattern space into a decision space in which the performance of traditional robust classifiers can be enhanced.",
-
notes = "Also known as
\cite{10.5120/ijca2017914276}
www.ijcaonline.org
Military Technical College, Cairo",
