Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery
| aut.embargo | No | en |
| aut.thirdpc.contains | No | |
| aut.thirdpc.permission | No | |
| aut.thirdpc.removed | No | |
| dc.contributor.advisor | Kasabov, Nikola | |
| dc.contributor.author | Liang, Wen | |
| dc.date.accessioned | 2009-10-20T03:44:17Z | |
| dc.date.available | 2009-10-20T03:44:17Z | |
| dc.date.copyright | 2009 | |
| dc.date.issued | 2009 | |
| dc.description.abstract | “Machine learning is the process of discovering and interpreting meaningful information, such as new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Larose, 2005). From my understanding, machine learning is a process of using different analysis techniques to observe previously unknown, potentially meaningful information, and discover strong patterns and relationships from a large dataset. Professor Kasabov (2007b) classified computational models into three categories (e.g. global, local, and personalised) which have been widespread and used in the areas of data analysis and decision support in general, and in the areas of medicine and bioinformatics in particular. Most recently, the concept of personalised modelling has been widely applied to various disciplines such as personalised medicine, personalised drug design for known diseases (e.g. cancer, diabetes, brain disease, etc.) as well as for other modelling problems in ecology, business, finance, crime prevention, and so on. The philosophy behind the personalised modelling approach is that every person is different from others, thus he/she will benefit from having a personalised model and treatment. However, personalised modelling is not without issues, such as defining the correct number of neighbours or defining an appropriate number of features. As a result, the principal goal of this research is to study and address these issues and to create a novel framework and system for personalised modelling. The framework would allow users to select and optimise the most important features and nearest neighbours for a new input sample in relation to a certain problem based on a weighted variable distance measure in order to obtain more precise prognostic accuracy and personalised knowledge, when compared with global modelling and local modelling approaches. | |
| dc.identifier.uri | https://hdl.handle.net/10292/749 | |
| dc.language.iso | en | en |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Optimisation | |
| dc.subject | Personalised modelling | |
| dc.subject | Feature selection | |
| dc.subject | Nearest neighbour | |
| dc.subject | Model optimization | |
| dc.title | Integrated feature, neighbourhood, and model optimization for personalised modelling and knowledge discovery | |
| dc.type | Thesis | |
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.level | Masters Theses | |
| thesis.degree.name | Master of Computer and Information Sciences |