dc.contributor.author | Nguyen, TT | |
dc.contributor.author | Ha, TS | |
dc.contributor.author | Luong, AV | |
dc.contributor.author | Liew, AWC | |
dc.contributor.author | Van Nguyen, TM | |
dc.contributor.author | McCall, J | |
dc.contributor.editor | LopezIbanez, M | |
dc.date.accessioned | 2020-01-31T05:57:06Z | |
dc.date.available | 2020-01-31T05:57:06Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 9781450361118 | |
dc.identifier.doi | 10.1145/3321707.3321770 | |
dc.identifier.uri | http://hdl.handle.net/10072/391053 | |
dc.description.abstract | In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significantly depends on the choice of meta-classifier. Normally, the classifier selection method applied to an ensemble usually removes all the predictions of a classifier if this classifier is not selected in the final ensemble. Here we present an idea to only remove a subset of each classifier's prediction thereby introducing a simultaneous meta-data and meta-classifier selection method for ensemble systems. Our approach uses Cross Validation on the training set to generate meta-data as the predictions of base classifiers. We then use Ant Colony Optimization to search for the optimal subset of meta-data and meta-classifier for the data. By considering each column of meta-data, we construct the configuration including a subset of these columns and a meta-classifier. Specifically, the columns are selected according to their corresponding pheromones, and the meta-classifier is chosen at random. The classification accuracy of each configuration is computed based on Cross Validation on meta-data. Experiments on UCI datasets show the advantage of proposed method compared to several classifier and feature selection methods for ensemble systems. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.publisher | Association for Computing Machinery (ACM) | |
dc.relation.ispartofconferencename | Genetic and Evolutionary Computation Conference (GECCO '19) | |
dc.relation.ispartofconferencetitle | GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference | |
dc.relation.ispartofdatefrom | 2019-07-13 | |
dc.relation.ispartofdateto | 2019-07-17 | |
dc.relation.ispartoflocation | Prague, Czech Republic | |
dc.relation.ispartofpagefrom | 39 | |
dc.relation.ispartofpagefrom | 8 pages | |
dc.relation.ispartofpageto | 46 | |
dc.relation.ispartofpageto | 8 pages | |
dc.subject.fieldofresearch | Neural networks | |
dc.subject.fieldofresearchcode | 461104 | |
dc.title | Simultaneous meta-data and meta-classifier selection in multiple classifier system | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Nguyen, TT; Ha, TS; Luong, AV; Liew, AWC; Van Nguyen, TM; McCall, J, Simultaneous meta-data and meta-classifier selection in multiple classifier system, GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019, pp. 39-46 | |
dc.date.updated | 2020-01-31T03:39:22Z | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Liew, Alan Wee-Chung | |