FCknn: A granular knn classifier based on formal concepts

Author

Kaburlasos, Vassilis G. ; Tsoukalas, Vassilis ; Moussiades, Lefteris

Author_Institution

Dept. of Comput. & Inf. Eng., Eastern Macedonia & Thrace Inst. of Technol., Kavala, Greece

fYear

2014

fDate

6-11 July 2014

Firstpage

61

Lastpage

68

Abstract

Recent work has proposed an enhancement of Formal Concept Analysis (FCA) in a tunable, hybrid formal context including both numerical and nominal data [1]. This work introduces FCknn, that is a granular knn classifier based on hybrid concepts, whose effectiveness is demonstrated on benchmark datasets from the literature including both numerical and nominal data. Preliminary experimental results compare well with the results by alternative classifiers from the literature. Formal concepts are interpreted as descriptive decision-making knowledge (rules) induced from the data.

Keywords

formal concept analysis; fuzzy set theory; numerical analysis; pattern classification; FCA; FCknn; benchmark datasets; descriptive decision-making knowledge rules; formal concept analysis; granular knn classifier; hybrid concepts; nominal data; numerical data; Context; Cost accounting; Extraterrestrial measurements; Lattices; Testing; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4799-2073-0

Type

conf

DOI

10.1109/FUZZ-IEEE.2014.6891726

Filename

6891726