http://www.cnr.it/ontology/cnr/individuo/prodotto/ID208803
Differential Evolution for automatic rule extraction from medical databases (Articolo in rivista)
- Type
- Label
- Differential Evolution for automatic rule extraction from medical databases (Articolo in rivista) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1016/j.asoc.2012.10.022 (literal)
- Alternative label
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Ivanoe De Falco (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.sciencedirect.com/science/article/pii/S1568494612004747 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
- Note
- ISI Web of Science (WOS) (literal)
- Scopu (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Institute of High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), 80131 Naples, Italy (literal)
- Titolo
- Differential Evolution for automatic rule extraction from medical databases (literal)
- Abstract
- In this paper, a new approach based on Differential Evolution (DE) for the automatic classification of
items in medical databases is proposed. Based on it, a tool called DEREx is presented, which automatically
extracts explicit knowledge from the database under the form of IF-THEN rules containing
AND-connected clauses on the database variables. Each DE individual codes for a set of rules. For each
class more than one rule can be contained in the individual, and these rules can be seen as logically
connected in OR. Furthermore, all the classifying rules for all the classes are found all at once in one
step. DEREx is thought as a useful support to decision making whenever explanations on why an item is
assigned to a given class should be provided, as it is the case for diagnosis in the medical domain. The
major contribution of this paper is that DEREx is the first classification tool in literature that is based on
DE and automatically extracts sets of IF-THEN rules without the intervention of any other mechanism.
In fact, all other classification tools based on DE existing in literature either simply find centroids for the
classes rather than extracting rules, or are hybrid systems in which DE simply optimizes some parameters
whereas the classification capabilities are provided by other mechanisms. For the experiments eight
databases from the medical domain have been considered. First, among ten classical DE variants, the
most effective of them in terms of highest classification accuracy in a ten-fold cross-validation has been
found. Secondly, the tool has been compared over the same eight databases against a set of fifteen classifiers
widely used in literature. The results have proven the effectiveness of the proposed approach, since
DEREx turns out to be the best performing tool in terms of highest classification accuracy. Also statistical
analysis has confirmed that DEREx is the best classifier. When compared to the other rule-based classification
tools here used, DEREx needs the lowest average number of rules to face a problem, and the
average number of clauses per rule is not very high. In conclusion, the tool here presented is preferable
to the other classifiers because it shows good classification accuracy, automatically extracts knowledge,
and provides users with it under an easily comprehensible form. (literal)
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