Individualised modelling using transductive inference and genetic algorithms : this thesis is presented as a part of the requirements for the award of the degree of Master of Information Technology at the Auckland University of Technology, June 2005 / Nisha Mohan ; supervisor: Nik Kasabov.
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 i...
I tiakina i:
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Kaituhi rangatōpū: | |
Hōputu: | iTuhinga whakapae |
Reo: | English |
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Urunga tuihono: | Full version thesis for AUT students and staff only |
Whakarāpopototanga: | 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. Author supplied keywords: Genetic algorithms; Inference. |
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Whakaahuatanga ōkiko: | 1 online resource Also held in print (137 leaves, 30 cm.) in in off-campus storage, box no. 175 (T 519.7 MOH) |
Rārangi puna kōrero: | Includes bibliographical references. |