INTRODUCTION: Gene expression data analysis is a critical aspect of disease prediction and classification, playing pivotal role in the field bioinformatics biomedical research. High-dimensional gene datasets hold wealth information, but their effective utilization hindered by presence irrelevant dimensions noise. The challenge lies extracting meaningful features from these to enhance accuracy c...

Hausdorr metrics are used in geometric settings for measuring the distance between sets of points. They have been used extensively in areas such as computer vision , pattern recognition and computational chemistry. While computing the distance between a single pair of sets under the Hausdorr metric has been well studied, no results were known for the Nearest Neighbor problem under Hausdorr metr...

Stability has been of a great concern in statistics: similar statistical conclusions should be drawn based on different data sampled from the same population. In this article, we introduce a general measure of classification instability (CIS) to capture the sampling variability of the predictions made by a classification procedure. The minimax rate of CIS is established for general plug-in clas...

The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

NEAREST NEIGHBOR IMPUTATION Jiahua Chen1 University of Waterloo Jun Shao2 University of Wisconsin-Madison Abstract Nearest neighbor imputation is one of the hot deck methods used to compensate for nonresponse in sample surveys. Although it has a long history of application, theoretical properties of the nearest neighbor imputation method are unknown prior to the current paper. We show that unde...

This work examines the nearest neighbor encoding problem with an unstructured codebook of arbitrary size and vector dimension. We propose a new tree-structured nearest neighbor encoding method that significantly reduces the complexity of the full-search method without any performance degradation in terms of distortion. Our method consists of efficient algorithms for constructing a binary tree f...

In this paper, we address the problem of active learning using the notion of influence sets based on Reverse Nearest Neighbor. Active learning is an area of machine learning which emphasizes on achieving optimal classification performance using as few labeled samples as possible. Reverse nearest neighbors have been used in domains such as clustering and outliers detection in the past effectivel...

In this work we propose a new method to create neural network ensembles. Our methodology develops over the conventional technique of bagging, where multiple classifiers are trained using a single training data set by generating multiple bootstrap samples from the training data. We propose a new method of sampling using the k-nearest neighbor density estimates. Our sampling technique gives rise ...

k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN classifier may decrease the precision of classification because of the uneven density of t raining samples .In view of the defect, an improved k-nearest neighbor algorithm is presented using shared nearest neighbor similarity which can compute similarity between test ...