dc.contributor.author | Karatsiolis, Savvas | en |
dc.contributor.author | Schizas, Christos N. | en |
dc.creator | Karatsiolis, Savvas | en |
dc.creator | Schizas, Christos N. | en |
dc.date.accessioned | 2019-11-13T10:40:39Z | |
dc.date.available | 2019-11-13T10:40:39Z | |
dc.date.issued | 2012 | |
dc.identifier.isbn | 978-1-4673-4358-9 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54226 | |
dc.description.abstract | The problem of diagnosing Pima Indian Diabetes from data obtained from the UCI Repository of Machine Learning Databases[6] is handled with a modified Support Vector Machine strategy. Performance comparison with previous studies is presented in order to demonstrate the proposed algorithm's advantages over various classification methods. The goal of the paper is to provide the grasp of a methodology that can be efficiently used to raise classification success rates obtained by the use of conventional approaches such as Neural Networks, RBF networks and K-nearest neighbors. The suggested algorithm divides the training set into two subsets: one that arises from the joining of coherent data regions and one that comprises of the data portion that is difficult to be clustered. Consequently, the first subset is used to train a Support Vector Machine with a RBF kernel and the second subset is used to train another Support Vector Machine with a polynomial kernel. During classification the algorithm is capable of identifying which of the two Support Vector Machine models to use. The intuition behind the suggested algorithm relies on the expectation that the RBF Support Vector Machine model is more appropriate to use on data sets of different characteristics than the polynomial kernel. In the specific study case the suggested algorithm raised average classification success rate to 82.2% while the best performance obtained by previous studies was 81% given by a fine tuned highly complex ARTMAP-IC model. © 2012 IEEE. | en |
dc.source | IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012 | en |
dc.source | 12th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2012 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872865949&doi=10.1109%2fBIBE.2012.6399663&partnerID=40&md5=8b13c830fa5a4574d8b84b7e349e5fc1 | |
dc.subject | Algorithms | en |
dc.subject | Diagnosis | en |
dc.subject | Bioinformatics | en |
dc.subject | Data sets | en |
dc.subject | Support vector machines | en |
dc.subject | Radial basis function networks | en |
dc.subject | Conventional approach | en |
dc.subject | Classification methods | en |
dc.subject | Clustering | en |
dc.subject | K-nearest neighbors | en |
dc.subject | Pima Indian Diabetes | en |
dc.subject | Coherent data | en |
dc.subject | Machine-learning database | en |
dc.subject | Performance comparison | en |
dc.subject | Polynomial kernels | en |
dc.subject | RBF kernels | en |
dc.subject | Region-based | en |
dc.subject | Study case | en |
dc.subject | Support vector | en |
dc.subject | Support Vector Machine | en |
dc.subject | Support vector machine algorithm | en |
dc.subject | Support Vector Machine Kernel | en |
dc.subject | Training sets | en |
dc.subject | UCI repository | en |
dc.title | Region based Support Vector Machine algorithm for medical diagnosis on Pima Indian Diabetes dataset | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/BIBE.2012.6399663 | |
dc.description.startingpage | 139 | |
dc.description.endingpage | 144 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Conference code: 95206 | en |
dc.description.notes | Cited By :14</p> | en |
dc.contributor.orcid | Schizas, Christos N. [0000-0001-6548-4980] | |
dc.gnosis.orcid | 0000-0001-6548-4980 | |