Repository logo
 

Individualised Modelling Using Transductive Inference and Genetic Algorithms

Files

Whole thesis(1.38 MB)

Date

2005

Authors

Supervisor

Kasabov, Nikola

Item type

Thesis

Degree name

Master of Information Technology

Journal Title

Journal ISSN

Volume Title

Publisher

Auckland University of Technology

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.

Description

Keywords

Genetic algorithms, Inference

Source

DOI

Publisher's version

Rights statement

Permanent link

https://hdl.handle.net/10292/11444

Collections

Masters Theses