Multi-class Nearest Neighbour Classifier for Incomplete Data Handling

被引：21

作者：

Nowak, Bartosz A. ^{[1
,2
]}

Nowicki, Robert K. ^{[2
]}

Wozniak, Marcin ^{[3
]}

Napoli, Christian ^{[4
]}

机构：

[1] Univ Warmia & Mazury, Dept Math Methods Comp Sci, PL-10710 Olsztyn, Poland

[2] Czestochowa Tech Univ, Inst Computat Intelligence, PL-42200 Czestochowa, Poland

[3] Silesian Tech Univ, Inst Math, PL-44101 Gliwice, Poland

[4] Univ Catania, Dept Math & Informat, I-95125 Catania, Italy

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT I | 2015年 / 9119卷

关键词：

Nearest neighbour; Missing values; Rough sets; NEURO-FUZZY SYSTEMS; MESOPOROUS SILICA;

D O I：

10.1007/978-3-319-19324-3_42

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The basic nearest neighbour algorithm has been designed to work with complete data vectors. Moreover, it is assumed that each reference sample as well as classified sample belong to one and the only one class. In the paper this restriction has been dismissed. Through incorporation of certain elements of rough set and fuzzy set theories into k-nn classifier we obtain a sample based classifier with new features. In processing incomplete data, the proposed classifier gives answer in the form of rough set, i. e. indicated lower or upper approximation of one or more classes. The basic nearest neighbour algorithm has been designed to work with complete data vectors and assumed that each reference sample as well as classified sample belongs to one and the only one class. Indication of more than one class is a result of incomplete data processing as well as final reduction operation.

引用

页码：469 / 480

页数：12

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