Instance-Based Learning Algorithms
Machine Learning
Matrix computations (3rd ed.)
A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Scientific Computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines
A Survey And Analysis Of Diversity Measures In Genetic Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Inference for the Generalization Error
Machine Learning
Genetic Algorithms as a Tool for Restructuring Feature Space Representations
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Results of the KDD'99 classifier learning
ACM SIGKDD Explorations Newsletter
Winning the KDD99 classification cup: bagged boosting
ACM SIGKDD Explorations Newsletter
KDD-99 classifier learning contest LLSoft's results overview
ACM SIGKDD Explorations Newsletter
The MP13 approach to the KDD'99 classifier learning contest
ACM SIGKDD Explorations Newsletter
Computer security and intrusion detection
Crossroads
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
MOGE: GP classification problem decomposition using multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set
Intelligent Data Analysis
An autonomous GP-based system for regression and classification problems
Applied Soft Computing
A generic multi-dimensional feature extraction method using multiobjective genetic programming
Evolutionary Computation
Corrections to "Pareto-based multiobjective machine learning: An overview and case studies"
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The influence of mutation on population dynamics in multiobjective genetic programming
Genetic Programming and Evolvable Machines
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Pattern Analysis & Applications
A generic optimising feature extraction method using multiobjective genetic programming
Applied Soft Computing
Linear Genetic Programming
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EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A distance sum-based hybrid method for intrusion detection
Applied Intelligence
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In this paper we investigate using multi-objective genetic programming to evolve a feature extraction stage for multiple-class classifiers. We find mappings which transform the input space into a new, multi-dimensional decision space to increase the discrimination between all classes; the number of dimensions of this decision space is optimized as part of the evolutionary process. A simple and fast multi-class classifier is then implemented in this multi-dimensional decision space. Mapping to a single decision space has significant computational advantages compared to k-class-to-2-class decompositions; a key design requirement in this work has been the ability to incorporate changing priors and/or costs associated with mislabeling without retraining. We have employed multi-objective optimization in a Pareto framework incorporating solution complexity as an independent objective to be minimized in addition to the main objective of the misclassification error. We thus give preference to simpler solutions which tend to generalize well on unseen data, in accordance with Occam's Razor. We obtain classification results on a series of benchmark problems which are essentially identical to previous, more complex decomposition approaches. Our solutions are much simpler and computationally attractive as well as able to readily incorporate changing priors/costs. In addition, we have also applied our approach to the KDD-99 intrusion detection dataset and obtained results which are highly competitive with the KDD-99 Cup winner but with a significantly simpler classification framework.