The nearest neighbor (NN) classiiers, especially the k-NN algorithm, are among the simplest and yet most eecient classiication rules and are widely used in practice. We introduce three adaptation rules that can be used in iterative training of a k-NN classiier. This is a novel approach both from the statistical pattern recognition and the supervised neural network learning points of view. The s...

Given a set P of N points in a ddimensional space, along with a query point q, it is often desirable to find k points of P that are with high probability close to q. This is the Approximate k-NearestNeighbors problem. We present two algorithms for AkNN. Both require O(Nd) preprocessing time. The first algorithm has a query time cost that is O(d+logN), while the second has a query time cost that...

Complex networks, such as biological, social, and communication networks, often entail uncertainty, and thus, can be modeled as probabilistic graphs. Similar to the problem of similarity search in standard graphs, a fundamental problem for probabilistic graphs is to efficiently answer k-nearest neighbor queries (k-NN), which is the problem of computing the k closest nodes to some specific node....

A k nearest neighbor (kNN) classifier classifies a query instance to the most frequent class of its k nearest neighbors in the training instance space. For imbalanced class distribution, a query instance is often overwhelmed by majority class instances in its neighborhood and likely to be classified to the majority class. We propose to identify exemplar minority class training instances and gen...

Many sophisticated classification algorithms have been proposed. However, there is no clear methodology of comparing the results among different methods. According to our experiments on the popular datasets, k-NN with properly tuned parameters performs on average best. Tuning the parametres include the proper k, proper distance measure and proper weighing functions. k-NN has a zero training tim...

Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L2 norm. One important variant of this query type is kNN query, which returns k data points with the minimum distances. When taking the temporal dimension into account, the kNN query result may change over...