Automatic stellar spectral classification with multiple intelligent classifiers

Stellar Classification is based on their spectral characteristics. In order to improve performance rates previously reported, like those based on statistical analysis or data transformations, classifiers based on computational intelligence provide a high level of accuracy no matter the presented high level of non-linearity or high dimensionality characteristics of data. In this paper, the star's classification is based on the use of three main strategies: multi-layer perceptrons (MLP), one nearest neighbor (1-NN), and support vector machines (SVM). A strategy of one-vs-one (OVO) for binary classification and directed acyclic graphs (DAG), improves the accuracy of classification and reduce the computational cost.