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HIERARCHICAL DECOMPOSITION OF MULTICLASS PROBLEMS
(ACAD SCIENCES CZECH REPUBLIC, INST COMPUTER SCIENCEPRAGA, 2008)
Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems ...
Building binary-tree-based multiclass classifiers using separability measures
(ELSEVIER SCIENCE BV, 2010)
Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification problems. However, a large number of real problems present more than ...
Evolutionary tuning of SVM parameter values in multiclass problems
(ELSEVIER SCIENCE BV, 2008)
Support vector machines (SVMs) were originally formulated for the solution of binary classification problems. In multiclass problems, a decomposition approach is often employed, in which the multiclass problem is divided ...
Multiclass Classification of Malicious Domains Using Passive DNS with XGBoost: (Work in Progress)
(2020-11-24)
The Domain Name System (DNS) protocol provides the mapping between hostnames and Internet Protocol addresses and vice versa. However, attackers use the DNS structure to register malicious domains to engage in malicious ...
Multiclass Classification of Malicious Domains Using Passive DNS with XGBoost (Work in Progress)
(Ieee, 2020-01-01)
The Domain Name System (DNS) protocol provides the mapping between hostnames and Internet Protocol addresses and vice versa. However, attackers use the DNS structure to register malicious domains to engage in malicious ...
A review on the combination of binary classifiers in multiclass problems
(SPRINGER, 2008)
Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier ...
Feature selection on wide multiclass problems using OVA-RFE
(Sociedad Iberoamericana de Inteligencia Artificial, 2009-12)
Feature selection is a pre–processing technique commonly used with high–dimensional datasets. It is aimed at reducing the dimensionality of the input space, discarding useless or redundant variables, in order to increase ...
Recopnocimiento de un producto de supermercado
(CIMAT, 2012)