Adaptive local hyperplane algorithm for learning small medical data sets

被引：0

作者：

Yang, Tao ^{[1
]}

Kecman, Vojislav ^{[1
]}

机构：

[1] Univ Auckland, Sch Engn, Auckland 1, New Zealand

来源：

EXPERT SYSTEMS | 2009年 / 26卷 / 04期

关键词：

small data set learning; classification; cancer; CLASSIFICATION;

D O I：

10.1111/j.1468-0394.2009.00494.x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

It is not unique that only a few samples from medical studies are available for knowledge discovery. Hence, a suitable classifier for the small data set learning problem is very interesting in medical work. In this paper, we experiment with the adaptive local hyperplane algorithm on small medical data sets. The experimental results on two cancer data sets demonstrate that the proposed classifier outperforms, on average, all the other four benchmarking classifiers for learning small data sets.

引用

页码：355 / 359

页数：5

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