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http://hdl.handle.net/1942/23693
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DC Field | Value | Language |
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dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | Falcon, Rafael | - |
dc.contributor.author | PAPAGEORGIOU, Elpiniki | - |
dc.contributor.author | Bello, Rafael | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.date.accessioned | 2017-05-17T08:30:20Z | - |
dc.date.available | 2017-05-17T08:30:20Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 85, p. 79-96 | - |
dc.identifier.issn | 0888-613X | - |
dc.identifier.uri | http://hdl.handle.net/1942/23693 | - |
dc.description.abstract | Rough Cognitive Networks are granular classifiers stemming from the hybridization of Fuzzy Cognitive Maps and Rough Set Theory. Such cognitive neural networks attempt to quantify the impact of rough granular constructs (i.e., the positive, negative and boundary regions of a target concept) over each decision class for the problem at hand. In rough classifiers, determining the precise granularity level is crucial to compute high prediction rates. Regrettably, learning the similarity threshold parameter requires reconstructing the information granules, which may be time-consuming. In this paper, we put forth a new multiclassifier system classifier named Rough Cognitive Ensembles. The proposed ensemble employs a collection of Rough Cognitive Networks as base classifiers, each operating at a different granularity level. This allows suppressing the requirement of learning a similarity threshold. We evaluate the granular ensemble with 140 traditional classification datasets using different heterogeneous distance functions. After comparing the proposed model to 15 well-known classifiers, the experimental evidence confirms that our scheme yields very promising classification rates. | - |
dc.description.sponsorship | This work was supported by the Research Council of Hasselt University. The authors would like to thank the anonymous reviewers for their constructive remarks throughout the revision process. | - |
dc.language.iso | en | - |
dc.rights | © 2017 Elsevier Inc. All rights reserved. | - |
dc.subject.other | machine learning; granular computing; rough set theory; fuzzy cognitive maps; rough cognitive networks; ensemble learning | - |
dc.title | Rough cognitive ensembles | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 96 | - |
dc.identifier.spage | 79 | - |
dc.identifier.volume | 85 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Napoles, G (reprint author), Hasselt Univ, Fac Business Econ, Hasselt, Belgium. gonzalo.napoles@uhasselt.be | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1016/j.ijar.2017.03.011 | - |
dc.identifier.isi | 000401396400006 | - |
dc.identifier.url | https://www.researchgate.net/publication/315613667_Rough_Cognitive_Ensembles?ev=prf_high | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2018 | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | Falcon, Rafael | - |
item.contributor | PAPAGEORGIOU, Elpiniki | - |
item.contributor | Bello, Rafael | - |
item.contributor | VANHOOF, Koen | - |
item.fullcitation | NAPOLES RUIZ, Gonzalo; Falcon, Rafael; PAPAGEORGIOU, Elpiniki; Bello, Rafael & VANHOOF, Koen (2017) Rough cognitive ensembles. In: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 85, p. 79-96. | - |
crisitem.journal.issn | 0888-613X | - |
crisitem.journal.eissn | 1873-4731 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Rough Cognitive Ensembles.pdf | Peer-reviewed author version | 607.07 kB | Adobe PDF | View/Open |
a.pdf Restricted Access | Published version | 1.57 MB | Adobe PDF | View/Open Request a copy |
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