Learning An Optimized Classification SystemFrom A Data Base Of Time Series Patterns UsingGenetic Algorithms
Cláudio M.N.A. Pereira, Roberto Schirru, Aquilino Senra Martinez · WIT transactions on information and communication technologies · 1970
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 classification. The goal of this model is to find how many and which are the clusters to consider. To validate the methodology, reference problems, where the best solution is wellknown, are proposed. Extending the scope of the application, the methodology is applied to a real problem, in which it is required to distinguish three nuclear accidents that may occur in a nuclear power plant. The misclassification rate was 5% in a total of 180 trials. To ratify the results an artificial neural network was designed and trained to solve the same problem. The results and comparisons are shown and commented. Transactions on Information and Communications Technologies vol 19 © 1998 WIT Press, www.witpress.com, ISSN 1743-3517