Algorithms for clustering data
Algorithms for clustering data
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Optimal linear combinations of neural networks
Neural Networks
Detecting masquerades in intrusion detection based on unpopular commands
Information Processing Letters
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A genetic integrated fuzzy classifier
Pattern Recognition Letters - Special issue: Advances in pattern recognition
New similarity rules for mining data
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
A Fuzzy One Class Classifier for Multi Layer Model
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
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This paper introduces a parallel combination of N 2 one class fuzzy KNN(FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNN's, that differ in the kind of similarity used. We tested the integration techniques in the case of N= 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration respect to the crisp one.