Mapping classifiers and datasets
| dc.authorid | 0000-0001-5838-4615 | |
| dc.contributor.author | Yıldız, Olcay Taner | en_US |
| dc.date.accessioned | 2015-01-15T23:01:52Z | |
| dc.date.available | 2015-01-15T23:01:52Z | |
| dc.date.issued | 2011-04 | |
| dc.department | Işık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.department | Işık University, Faculty of Engineering, Department of Computer Engineering | en_US |
| dc.description.abstract | Given the posterior probability estimates of 14 classifiers on 38 datasets, we plot two-dimensional maps of classifiers and datasets using principal component analysis (PCA) and Isomap. The similarity between classifiers indicate correlation (or diversity) between them and can be used in deciding whether to include both in an ensemble. Similarly, datasets which are too similar need not both be used in a general comparison experiment. The results show that (i) most of the datasets (approximately two third) we used are similar to each other, (ii) multilayer perceptrons and k-nearest neighbor variants are more similar to each other than support vector machine and decision tree variants. (iii) the number of classes and the sample size has an effect on similarity. | en_US |
| dc.description.version | Publisher's Version | en_US |
| dc.description.version | Author Pre-Print | en_US |
| dc.identifier.citation | Yıldız, O. T. (2011). Mapping classifiers and datasets. Expert Systems with Applications, 38(4), 3697-3702. doi:10.1016/j.eswa.2010.09.027 | en_US |
| dc.identifier.doi | 10.1016/j.eswa.2010.09.027 | |
| dc.identifier.endpage | 3702 | |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.issue | 4 | |
| dc.identifier.scopus | 2-s2.0-78650689421 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 3697 | |
| dc.identifier.uri | https://hdl.handle.net/11729/417 | |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.eswa.2010.09.027 | |
| dc.identifier.volume | 38 | |
| dc.identifier.wos | WOS:000286904600087 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.indekslendigikaynak | Science Citation Index Expanded (SCI-EXPANDED) | en_US |
| dc.institutionauthor | Yıldız, Olcay Taner | en_US |
| dc.institutionauthorid | 0000-0001-5838-4615 | |
| dc.language.iso | en | en_US |
| dc.peerreviewed | Yes | en_US |
| dc.publicationstatus | Published | en_US |
| dc.publisher | Pergamon-Elsevier Science Ltd | en_US |
| dc.relation.ispartof | Expert Systems With Applications | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Classifiers | en_US |
| dc.subject | Datasets | en_US |
| dc.subject | No free lunch theorem | en_US |
| dc.subject | PCA | en_US |
| dc.subject | Isomap | en_US |
| dc.subject | Learning algorithms | en_US |
| dc.subject | Classification (of information) | en_US |
| dc.subject | Learning systems | en_US |
| dc.subject | Complexity measures | en_US |
| dc.subject | Data sets | en_US |
| dc.subject | K-nearest neighbors | en_US |
| dc.subject | Multi-layer perceptrons | en_US |
| dc.subject | Posterior probability | en_US |
| dc.subject | Sample sizes | en_US |
| dc.subject | Two-dimensional map | en_US |
| dc.subject | Decision trees | en_US |
| dc.subject | Pattern recognition systems | en_US |
| dc.subject | Principal component analysis | en_US |
| dc.subject | Probability | en_US |
| dc.subject | Classifiers | en_US |
| dc.title | Mapping classifiers and datasets | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication |
