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...

• Type: Journal
• Year: 2008
• Where: JMLR
• Authors: Elena Marchiori
• Keywords: Instance Selection, JMLR 2008, Nearest Neighbor, Training