Techniques for data pattern selection and abstraction

Nikolaidis, Konstantinos
Techniques for data pattern selection and abstraction. Doctor of Philosophy thesis, University of Liverpool.

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This thesis concerns the problem of prototype reduction in instance-based learning. In order to deal with problems such as storage requirements, sensitivity to noise and computational complexity, various algorithms have been presented that condense the number of stored prototypes, while maintaining competent classification accuracy. Instance selection, which recovers a smaller subset of the original training set, is the most widely used technique for instance reduction. But, prototype abstraction that generates new prototypes to replace the initial ones has also gained a lot of interest recently. The major contribution of this work is the proposal of four novel frameworks for performing prototype reduction, the Class Boundary Preserving algorithm (CBP), a hybrid method that uses both selection and generation of prototypes, Instance Seriation for Prototype Abstraction (ISPA), which is an abstraction algorithm, and two selective techniques, Spectral Instance Reduction (SIR) and Direct Weight Optimization (DWO). CBP is a multi-stage method based on a simple heuristic that is very effective in identifying samples close to class borders. Using a noise filter harmful instances are removed, while the powerful heuristic determines the geometrical distribution of patterns around every instance. Together with the concepts of nearest enemy pairs and mean shift clustering this algorithm decides on the final set of retained prototypes. DWO is a selection model whose output set of prototypes is decided by a set of binary weights. These weights are computed according to an objective function composed of the ratio between the nearest friend and nearest enemy of every sample. In order to obtain good quality results DWO is optimized using a genetic algorithm. ISPA is an abstraction technique that employs the concept of data seriation to organize instances in an arrangement that favours merging between them. As a result, a new set of prototypes is created. Results show that CBP, SIR and DWO, the three major algorithms presented in this thesis, are competent and efficient in terms of at least one of the two basic objectives, classification accuracy and condensation ratio. The comparison against other successful condensation algorithms illustrates the competitiveness of the proposed models. The SIR algorithm presents a set of border discriminating features (BDFs) that depicts the local distribution of friends and enemies of all samples. These are then used along with spectral graph theory to partition the training set in to border and internal instances.

Item Type: Thesis (Doctor of Philosophy)
Additional Information: Date: 2012-09 (completed)
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 05 Sep 2013 10:41
Last Modified: 17 Dec 2022 01:19
DOI: 10.17638/00010535
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