Information Theoretic Learning and local modeling for binary and multiclass classification

被引：2
作者：
Porto-Díaz, Iago [1 ]
Martínez-Rego, David [1 ]
Alonso-Betanzos, Amparo [1 ]
Fontenla-Romero, Oscar [1 ]
机构：
[1] Department of Computer Science, Facultade de Informática, University of A Coruña, Coruña, Campus de Elviña s/n
来源：
Progress in Artificial Intelligence | 2012年 / 1卷 / 04期
关键词：
Classification; FVQIT; Information theoretic learning; Local modeling; Machine learning;
D O I：
10.1007/s13748-012-0032-8
中图分类号：
学科分类号：
摘要：
In this paper, a learning model for binary and multiclass classification based on local modeling and Information Theoretic Learning (ITL) is described. The training algorithm for the model works on two stages: first, a set of nodes are placed on the frontiers between classes using a modified clustering algorithm based on ITL. Each of these nodes defines a local model. Second, several one-layer neural networks, associated with these local models, are trained to locally classify the points in its proximity. The method is successfully applied to problems with a large amount of instances and high dimension like intrusion detection and microarray gene expression. © 2012 Springer-Verlag Berlin Heidelberg.
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收藏
页码：315 / 328
页数：13
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