Paper Info
Title
A Multi-Label Voting Algorithm for Neuro-Fuzzy Classifier Ensembles with Applications in Visual Arts Data Mining
Abstract
The term visual arts data mining defines a framework for Data Mining techniques applied to learn and discover patterns in visual arts collections. Its results can be widely used by visual arts market, museums and art galleries. This paper proposes a multi-label voting algorithm to identify similar visual arts objects studied using neuro-fuzzy classifiers. The algorithm integrates predictions of experts trained on clusters of heterogeneous collections of data. It combines predictions of the modular ensemble of classifiers by identifying hierarchical votes for most similar classes. Experimental results show better performances than individual global models. Relationships between some visual arts patterns are inferred. We also compare the results obtained for few fusion versions of our algorithm with other methods applied on IRIS and Glass benchmarks. The results show that our algorithm has at least similar performance to other schemes on all data sets and adds flexibility to cases where classifiers' expertise overlaps on unions of disjunctive sets of the universe of discourse.
Year
DOI
Venue
2005
10.1109/ISDA.2005.10
ISDA
Keywords
Field
DocType
similar class,similar performance,term visual arts data,visual arts market,visual arts collection,visual arts data mining,multi-label voting algorithm,neuro-fuzzy classifier ensembles,visual arts pattern,data mining technique,similar visual arts object,neuro fuzzy,visual art,universe of discourse,art,data mining
Data mining,Data set,Voting algorithm,Visual arts,Pattern clustering,Computer science,Neuro fuzzy classifier,Artificial intelligence,Fuzzy neural nets,Modular design,Machine learning
Conference
ISBN
Citations 
PageRank 
0-7695-2286-06
1
0.35
References 
Authors
6
3
Name
Order
Citations
PageRank
Daniel Neagu1392.82
Shuai Zhang23711.44
Catalin Balescu330.80