| dc.contributor.author | Antunes, Mário | |
| dc.contributor.author | Silva, Catarina | |
| dc.contributor.author | Ribeiro, Bernardete | |
| dc.contributor.author | Correia, Manuel | |
| dc.date.accessioned | 2025-12-12T09:55:24Z | |
| dc.date.available | 2025-12-12T09:55:24Z | |
| dc.date.issued | 2011 | |
| dc.description.abstract | In this paper we propose and analyse methods for expanding state-of-the-art performance on text classification. We put forward an ensemble-based structure that includes Support Vector Machines (SVM) and Artificial Immune Systems (AIS). The underpinning idea is that SVM-like approaches can be enhanced with AIS approaches which can capture dynamics in models. While having radically different genesis, and probably because of that, SVM and AIS can cooperate in a committee setting, using a heterogeneous ensemble to improve overall performance, including a confidence on each system classification as the differentiating factor. Results on the well-known Reuters-21578 benchmark are presented, showing promising classification performance gains, resulting in a classification that improves upon all baseline contributors of the ensemble committee. | eng |
| dc.identifier.citation | Antunes, M., Silva, C., Ribeiro, B., Correia, M. (2011). A Hybrid AIS-SVM Ensemble Approach for Text Classification. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_36 | |
| dc.identifier.doi | 10.1007/978-3-642-20267-4_36 | |
| dc.identifier.isbn | 9783642202667 | |
| dc.identifier.isbn | 9783642202674 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.issn | 1611-3349 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/15001 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer Berlin Heidelberg | |
| dc.relation.hasversion | https://link.springer.com/chapter/10.1007/978-3-642-20267-4_36 | |
| dc.relation.ispartof | Lecture Notes in Computer Science | |
| dc.relation.ispartof | Adaptive and Natural Computing Algorithms | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Artificial Immune System | |
| dc.subject | Support Vector Machine | |
| dc.subject | Text Classification | |
| dc.subject | Tunable Activation Threshold | |
| dc.subject | Ensembles | |
| dc.subject | Hybrid System. | |
| dc.title | A Hybrid AIS-SVM Ensemble Approach for Text Classification | eng |
| dc.type | book part | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 352 | |
| oaire.citation.startPage | 342 | |
| oaire.citation.title | Adaptive and Natural Computing Algorithms. ICANNGA 2011. | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Antunes | |
| person.givenName | Mário | |
| person.identifier | R-000-NX4 | |
| person.identifier.ciencia-id | AF10-7EDD-5153 | |
| person.identifier.orcid | 0000-0003-3448-6726 | |
| person.identifier.scopus-author-id | 25930820200 | |
| relation.isAuthorOfPublication | e3e87fb0-d1d6-44c3-985d-920a5560f8c1 | |
| relation.isAuthorOfPublication.latestForDiscovery | e3e87fb0-d1d6-44c3-985d-920a5560f8c1 |
