Class Conditional Nearest Neighbor for Large Margin Instance Selection
This paper presents a relational framework for studying properties of labeled data points related to proximity and labeling information in order to improve the performance of the 1NN rule. Specifically, the class conditional nearest neighbor (ccnn) relation over pairs of points in a labeled training set is introduced. For a given class label c, this relation associates to each point a its nearest neighbor computed among only those points with class label c (excluded a). A characterization of ccnn in terms of two graphs is given. These graphs are used for defining a novel scoring function over instances by means of an information-theoretic divergence measure applied to the degree distributions of these graphs. The scoring function is employed to develop an effective large margin instance selection method, which is empirically demonstrated to improve storage and accuracy performance of the 1NN rule on artificial and real-life data sets.
computing methodologies artificial intelligence learning heuristics design machine learning