Measurement of Data Complexity for Classification Problems with Unbalanced Data

被引：46

作者：

Anwar, Nafees ^{[1
]}

Jones, Geoff ^{[1
]}

Ganesh, Siva ^{[1
]}

机构：

[1] Massey Univ, Inst Fundamental Sci Stat, Palmerston North 4412, Manawatu, New Zealand

来源：

STATISTICAL ANALYSIS AND DATA MINING | 2014年 / 7卷 / 03期

关键词：

class imbalance; complexity measurement; nearest neighbors; Bayes error; PROBABILITY; ERROR;

D O I：

10.1002/sam.11228

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

We introduce a complexity measure for classification problems that takes account of deterioration in classifier performance as a result of class imbalance. The measure is based on k-nearest neighbors. We explore the choices of k and the distance metric through a simulation study, and illustrate the use of our measure, and related data visualization techniques, with real datasets from the literature. (C) 2014 Wiley Periodicals, Inc.

引用

页码：194 / 211

页数：18

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