Title :
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
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;
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-2073-0
DOI :
10.1109/FUZZ-IEEE.2014.6891726
