A new signal classification technique by means of Genetic Algorithms and kNN
Daniel Rivero, Enrique Fernández-Blanco, Julián Dorado, Alejandro Pazos · 2011
Signal classification is based on the extraction of several features that will be used as inputs of a classifier. The selection of these features is one of the most crucial parts, because they will design the search space, and, therefore, will determine the difficulty of the classification. Usually, these features are selected by using some prior knowledge about the signals, but there is no method that can determine that they are the most appropriate to solve the problem. This paper proposes a new technique for signal classification in which a Genetic Algorithm is used in order to automatically select the best feature set for signal classification, in combination with a kNN as classifier system. This method was used in a well known problem and its results improve those already published in other works.