The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets. A usual way is to adaptively weight the three objectives as an objective function and then use a single-objective optimization method for achieving this goal. This paper proposes a multi-objective approach to cope with the weight tuning...

This paper discusses a genetic-algorithm-based approach for selecting a small number of representative instances from a given data set in a pattern classification problem. The genetic algorithm also selects a small number of significant features. That is, instances and features are simultaneously selected for finding a compact data set. The selected instances and features are used as a referenc...

In this paper, a novel algorithm for enhancing the performance of classification is proposed. This new method provides rich information for clustering and outlier detection. We call it Natural Nearest Neighbor with Quality (3N-Q). Comparing to K-nearest neighbor and E-nearest neighbor, 3N-Q employs a completely different concept to find the nearest neighbors passively, which can adaptively and ...

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

The k-nearest-neighbor rule is a well known pattern recognition technique with very good results in a great variety of real classification tasks. Based on the neighborhood concept, several classification rules have been proposed to reduce the error rate of the k-nearest-neighbor rule (or its time requirements). In this work, two new geometrical neighborhoods are defined and the classification r...

The nearest neighbor classification is a simple and effective technique for pattern recognition. The performance of this technique is known to be sensitive to the distance function used in classifying a test instance. In this paper, we propose a technique to learn sample weights via maximizing classification consistency. Experimental analysis shows that the distance trained in this way enlarges...

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

To classify time series by nearest neighbor, we need to specify or learn a distance. We consider several variations of the Mahalanobis distance and the related Large Margin Nearest Neighbor Classification (LMNN). We find that the conventional Mahalanobis distance is counterproductive. However, both LMNN and the class-based diagonal Mahalanobis distance are competitive.

This paper studies nearest neighbor classification in a model where unlabeled data points arrive in a stream, and the learner decides, for each one, whether to ask for its label. Are there generic ways to augment or modify any selective sampling strategy so as to ensure the consistency of the resulting nearest neighbor classifier?

Using nearest neighbor classification with fault dictionaries to resolve inexact signature matches in digital circuit diagnosis is inadequate. Nearest neighbor focuses on the possible diagnoses rather than on the tests. Our alternative—the information flow model—focuses on test information in the fault dictionary to provide more accurate diagnostics.