Two Way Focused Classification

In this paper we propose TwoWayFocused classification that performs feature selection and tuple selection over the data before performing classification. Although feature selection and tuple selection have been studied earlier in various research areas such as machine learning, data mining, and so on, they have rarely been studied together. The contribution of this paper is that we propose a novel distance measure to select the most representative features and tuples. Our experiments are conducted over some microarray gene expression datasets, UCI machine learning and KDD datasets. Results show that the proposed method outperforms the existing methods quite significantly.

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