Construction of Multi-class Classifiers by Extreme Learning Machine Based One-class Classifiers

被引:0
|
作者
Gautam, Chandan [1 ]
Tiwari, Aruna [1 ]
Ravindran, Sriram [1 ]
机构
[1] Indian Inst Technol Indore, Dept Comp Sci & Engn, Indore, Madhya Pradesh, India
关键词
CLASSIFICATION; ENSEMBLE;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Construction of multi-class classifiers using homogeneous combination of Extreme Learning Machine (ELM) based one-class classifiers have been proposed in this paper. Each class has been trained using individual one-class classifier and any new sample will belong to that class, which will yield maximum value. Proposed methods can be used to detect unknown outliers using multi-class classifiers. Two recently proposed one-class classifiers viz., kernel and random feature mapping based one-class ELM, is extended for multi-class construction in this paper. Further, we construct one-class classifier based multi-class classifier in two ways: with rejection and without rejection of few samples during training. We also perform consistency based model selection for optimal parameters selection in one-class classifier. We have tested the generalization capability of the proposed classifiers on 6 synthetic datasets and two benchmark datasets.
引用
收藏
页码:2001 / 2007
页数:7
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