Feature selection with harmony search and its applications

Abstract

Feature selection is a term given to the problem of selecting important domain attributes which are most predictive of a given outcome. Unlike other dimensionality reduction methods, feature selection approaches seek to preserve the semantics of the original data following reduction. Many strategies have been exploited for this task in an effort to identify more compact and better quality feature subsets. A number of group-based feature subset evaluation measures have been developed, which have the ability to judge the quality of a given feature subset as a whole, rather than assessing the qualities of individual features. Techniques of stochastic nature have also emerged, which are inspired by nature phenomena or social behaviour, allowing good solutions to be discovered without resorting to exhaustive search.
In this thesis, a novel feature subset search algorithm termed “feature selection with harmony search” is presented. The proposed approach utilises a recently developed meta-heuristic: harmony search, that is inspired by the improvisation process of musical players. The proposed approach is general, and can be employed in conjunction with many feature subset evaluation measures. The simplicity of harmony search is exploited to reduce the overall complexity of the search process. The stochastic nature of the resultant technique also allows the search process to escape from local optima, while identifying multiple, distinctive candidate solutions. Additional parameter control schemes are introduced to reduce the effort and impact of static parameter configuration of HS, which are further combined with iterative refinement, in order to enforce the discovery of more compact feature subsets.
The flexibility of the proposed approach, and its powerful performance in selecting multiple, good quality feature subsets lead to a number of further theoretical developments. These include the generation and reduction of feature subset-based classifier ensembles; feature selection and adaptive classifier ensemble for dynamic data; hybrid rule induction on the basis of fuzzy-rough set theory; and antecedent selection for fuzzy rule interpolation. The resultant techniques are experimentally evaluated using data sets drawn from real-world problem domains, and systematically compared with leading methodologies in their respective areas, demonstrating the efficacy and competitive performance of the present work.

Date of Award	03 Feb 2014
Original language	English
Awarding Institution	Aberystwyth University
Supervisor	Qiang Shen (Supervisor) & Neil Mac Parthalain (Supervisor)