Multi-objective Evolutionary Optimization for Visual Data Mining with Virtual Reality Spaces: Application to Alzheimer Gene Expressions

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ConferenceGenetic and Evolutionary Computation Conference (GECCO) (a recombination of the 15th International Conference on Genetic Algorithms (ICGA) and the 11th Genetic Programming Conference (GP)), July 8-12, 2006., Seattle, Washington, USA
Subjectvisual data mining; virtual reality spaces; multi-objective optimization; genetic algorithms; NSGA-II algorithm; k-nn classification; cross-validation error; similarity structure preservation; non-linear mapping; Sammon error; Alzheimer disease; genomics; espaces de réalité virtuelle; optimisation multi objectif; algorithmes génétiques; algorithme NSGA II; classification k nn; erreur de validation croisée; préservation de la structure de similarité; fonction non linéai
AbstractThis paper introduces a multi-objective optimization approach to the problem of computing virtual reality spaces for the visual representation of relational structures (e.g. databases), symbolic knowledge and others, in the context of visual data mining and knowledge discovery. Procedures based on evolutionary computation are discussed. In particular, the NSGA-II algorithm is used as a framework for an instance of this methodology; simultaneously minimizing Sammon's error for dissimilarity measures, and mean cross-validation error on a k-nn pattern classifier. The proposed approach is illustrated with an example from genomics (in particular, Alzheimer's disease) by constructing virtual reality spaces resulting from multi-objective optimization. Selected solutions along the Pareto front approximation are used as nonlinearly transformed features for new spaces that compromise similarity structure preservation (from an unsupervised perspective) and class separability (froma supervised pattern recognition perspective), simultaneously. The possibility of spanning a range of solutions between these two important goals, is a benefit for the knowledge discovery and data understanding process. The quality of the set of discovered solutions is superior to the ones obtained separately, from the point of view of visual data mining.
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AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48506
NPARC number5765562
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Record identifierb2353854-74ac-4e95-86cd-8daffbab4b91
Record created2009-03-29
Record modified2016-05-09
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