Classification of Hydro Chemical Data in the Reduced Dimensional Space

Abstract

The main objective of this paper is to systematically analyze the performance of water samples classifications for different data representations. We compare the classification of full data representation to the classification of data items in lower dimensional space obtained by projection of original data on the space of first principal components and further on the space of centroids of classes. We use linear support vector machines for classification of ground water samples collected from five different localities of Odra River basin and results are evaluated by standard measures including recall, precision and F1measure.

Keywords

• concept decomposition
• dimensionality reduction
• principal components analysis
• support vector machines