DocumentCode :
2851078
Title :
Evaluating Ranking Composition Methods for Multi-Objective Optimization of Knowledge Rules
Author :
Giusti, Rafael ; Batista, Gustavo E A P A ; Prati, Ronaldo C.
Author_Institution :
Inst. of Math. & Comput. Sci., Univ. of Sao Paulo, Sao Carlos
fYear :
2008
fDate :
10-12 Sept. 2008
Firstpage :
537
Lastpage :
542
Abstract :
Most symbolic classifiers aim at building sets of rules with good coverage and precision. While this is suitable for most applications, they tend to neglect other desirable properties, such as the ability to induce novel knowledge or to show new points of view of well-established concepts. An approach to overcome these limitations involves using a multi-objective evolutionary algorithm to build knowledge rules with specific properties specified by the user. In this paper, we report a research work that combined evolutionary algorithms and ranking composition methods for multi-objective optimization. In this approach, candidate solutions are built, evaluated and ranked according to their performance in each individual objective. Then rankings are composed into a single ranking which reflects the candidate solutions´ ability to solve the multi-objective problem considering all objectives simultaneously. We investigate the behavior of 5 ranking composition methods. These methods are compared and we conclude that all of the studied ranking composition methods provide good balance of objectives. Moreover, for the 11 datasets analyzed, we conclude condorcet is the only method which performs statistically better than other methods.
Keywords :
evolutionary computation; learning (artificial intelligence); knowledge rules; multi objective evolutionary algorithm; multi objective optimization; ranking composition methods; symbolic classifiers; symbolic supervised learning; Application software; Buildings; Computer science; Data analysis; Data mining; Evolutionary computation; Hybrid intelligent systems; Mathematics; Optimization methods; Performance analysis; Evolutionary Computation; Machine Learning; Multi-objective Optimization; Ranking Composition; Rule Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-0-7695-3326-1
Electronic_ISBN :
978-0-7695-3326-1
Type :
conf
DOI :
10.1109/HIS.2008.154
Filename :
4626685
Link To Document :
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