Hit Miss Networks with Applications to Instance Selection

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Hit Miss Networks with Applications to Instance Selection
In supervised learning, a training set consisting of labeled instances is used by a learning algorithm for generating a model (classifier) that is subsequently employed for deciding the class label of new instances (for generalization). Characteristics of the training set, such as presence of noisy instances and size, influence the learning algorithm and affect generalization performance. This paper introduces a new network-based representation of a training set, called hit miss network (HMN), which provides a compact description of the nearest neighbor relation between each pair of classes. We show that structural properties of HMN's correspond to properties of training points related to the one nearest neighbor (1-NN) decision rule, such as being border or central point. This motivates us to use HMN's for improving the performance of a 1-NN classifier by removing instances from the training set (instance selection). We introduce three new algorithms based on HMN for instan...
Elena Marchiori
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where JMLR
Authors Elena Marchiori
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