The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it mus

Instance-based learning methods like the nearest neighbour classifier have been proven to perform well in pattern classification on many fields. Despite their high classification accuracy, they suffer from high storage requirement, computational cost and sensitivity to noise. In this paper, we present a data reduction method for classification techniques based on entropy-based partitioning and center instances. Experimental results show that the new algorithms achieve a high data reduction rate as well as classification accuracy.