Learning An Optimized Classification System From A Data Base Of Time Series Patterns Using Genetic Algorithms
Price
Free (open access)
Volume
22
Pages
14
Published
1998
Size
977 kb
Paper DOI
10.2495/DATA980031
Copyright
WIT Press
Author(s)
C.M.N.A. Pereira, R. Schirru & A.S. Martinez
Abstract
This work presents a novel methodology for pattern recognition that uses genetic learning to get an optimized classification system. Each class is represented by several time series in a data base. The idea is to find clusters in the set of the training patterns of each class so that their centroids can distinguish the classes with a minimum of misclassifications. Due to the high level of difficulty found in this optimization problem and the poor prior knowledge about the patterns domain, a model based on genetic algorithm is proposed to extract this knowledge, searching for the minimum number of subclasses that leads to a maximum correctness in the
Keywords
