Individualised modelling using transductive inference and genetic algorithms
| aut.embargo | No | en_NZ |
| dc.contributor.advisor | Kasabov, Nik | |
| dc.contributor.author | Mohan, Nisha | |
| dc.date.accessioned | 2018-03-12T23:46:47Z | |
| dc.date.available | 2018-03-12T23:46:47Z | |
| dc.date.copyright | 2005 | |
| dc.date.issued | 2005 | |
| dc.description.abstract | While inductive modeling is used to develop a model (function) from data of the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for every new input vector based on some closest vectors from the existing problem space. This individual model approximates the output value only for this input vector. However, deciding on the appropriate distance measure, number of nearest neighbours and a minimum set of important features/variables is a challenge and is usually based on prior knowledge or exhaustive trial and test experiments. Proposed algorithm – This thesis proposes a Genetic Algorithm (GA) method for optimising these three factors using a transductive approach. This novel approach called Individualised Modeling using Transductive Inference and Genetic Algorithms (IMTIGA) is tested on several datasets from UCI repository for classification task and real world scenario for pest establishment prognosis and results show that it outperforms conventional, inductive approaches of global and local modelling. | en_NZ |
| dc.identifier.uri | https://hdl.handle.net/10292/11444 | |
| dc.language.iso | en | en_NZ |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Genetic algorithms | en_NZ |
| dc.subject | Inference | en_NZ |
| dc.title | Individualised modelling using transductive inference and genetic algorithms | en_NZ |
| dc.type | Thesis | en_NZ |
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.name | Master of Information Technology | en_NZ |