Design of a two-stage fuzzy classification model
Expert Systems with Applications: An International Journal
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
Enhancing Tor's performance using real-time traffic classification
Proceedings of the 2012 ACM conference on Computer and communications security
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The design of algorithms that explore multiple representation languages and explore different search space has an intuitive appeal.In this context of classification problems, algorithmsthat generate multivariate trees are able to explore multiplerepresentation languages by using decision test based on acombination of attributes.The same applies to models threesalgorithms, in regression domains, but using linear models atleaf nodes.In this paper we study where to use combinations of attributes in decision tree learning.We present an algorithm for multivariate tree learning that combines a univariate decision tree with a discriminant function by means of constructiveinduction.This algorithm is able to use decision nodes with multivariate tests, and leaf nodes that predict a class using adiscrimnant. Multivariate decision nodes are built when growing the tree, while functional leaves are built when pruning the tree.Functional trees can be seen as a generalization of multivariate trees.Our algorithm was compared against to its components and two simplified versions using 30 benchmark datasets. The experimental evaluation shows that our algorithm has clear Advantages with respect to the generalization ability and model sizes at statistically significant.