The practice of river quality classification usually uses Water Quality Index (WQI) to evaluate the WQI values river. However, due huge data collection on pollution with uncertain water parameter values, need a different approach classify quality. One supervised algorithms known as K-Nearest Neighbors (KNN) seems give new for where each points are classified according k number or closest neighb...

We study the $k$ nearest neighbors problem in plane for general, convex, pairwise disjoint sites of constant description complexity such as line segments, disks, and quadrilaterals under a general family distance functions including $L_p$ norms additively weighted Euclidean distances. compose static data structure this setting with nearly optimal $O(n\log\log n)$ space, $O(\log n+k)$ query time...

The nearest neighbor (NN) approach is a powerfd nonparametric technique for pattern classification tasks. In this paper, algorithms for prototype reduction, hierarchical prototype organization and fast NN search are described. To remove redundant category prototypes and to avoid redundant comparisons, the algorithms exploit geometrical information of a given prototype set which is represented a...

The nearest neighbor classifier is a powerful, straightforward, and very popular approach to solving many classification problems. It also enables users to easily incorporate weights of training instances into its model, allowing users to highlight more promising examples. Instance weighting schemes proposed to date were based either on attribute values or external knowledge. In this paper, we ...

Missing data is an important issue in almost all fields of quantitative research. A nonparametric procedure that has been shown to be useful is the nearest neighbor imputation method. We suggest a weighted nearest neighbor imputation method based on Lq-distances. The weighted method is shown to have smaller imputation error than available NN estimates. In addition we consider weighted neighbor ...

The accuracy of the k-nearest neighbor algorithm depends on the distance function used to measure similarity between instances. Methods have been proposed in the literature to learn a good distance function from a labelled training set. One such method is the large margin nearest neighbor classifier that learns a global Mahalanobis distance. We propose a mixture of such classifiers where a gati...

In this paper, an optimized k nearest neighbor algorithm for the 2nd edition of the Large Scale Hierarchical Text Classification Pascal Challenge was summarized. Firstly, we perform k-NN algorithm on the datasets to obtain the top-k nearest neighbors for each testing documents. Secondly, several critical category-neighbors features were identified and the impact of each of those features were e...