The k-nearest neighbor rule is one of the simplest and most attractive pattern classification algorithms. It can be interpreted as an empirical Bayes classifier based on the estimated a posteriori probabilities from the k nearest neighbors. The performance of the k-nearest neighbor rule relies on the locally constant a posteriori probability assumption. This assumption, however, becomes problem...

Nearest neighbor (NN) classification assumes locally constant class conditional probabilities, and suffers from bias in high dimensions with a small sample set. In this paper, we propose a novel cam weighted distance to ameliorate the curse of dimensionality. Different from the existing neighborhood-based methods which only analyze a small space emanating from the query sample, the proposed nea...

Matrices are a common form of data encountered in a wide range of real applications. How to efficiently classify this kind of data is an important research topic. In this paper, we propose a novel distance metric learning method named two dimensional large margin nearest neighbor (2DLMNN), for improving the performance of k-nearest neighbor (KNN) classifier in matrix classification. Different f...

Building an index tree is a common approach to speed up the k nearest neighbour search in large databases of many-dimensional records. Many applications require varying distance metrics by putting a weight on diierent dimensions. The main problem with k nearest neighbour searches using weighted euclidean metrics in a high dimensional space is whether the searches can be done eeciently We presen...

The k-Nearest Neighbor is one of the simplest Machine Learning algorithms. Besides its simplicity, k-Nearest Neighbor is a widely used technique, being successfully applied in a large number of domains. In k-Nearest Neighbor, a database is searched for the most similar elements to a given query element, with similarity defined by a distance function. In this work, we are most interested in the ...

Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user’s design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Naïve Bayes, Decision Tree, and k-Nearest Neighbor. ...

The k nearest neighbor classification method is one of the humblest method in conceptually and it is a top method in image mining. In this work, the enhanced k nearest neighbor (EKNN) technique has been implemented to identify the cancer and automatic classification of benign and malignant tissues in the huge amount of lung cancer image datasets. In this proposed system, we have used three stag...

Nearest neighbor algorithms can be implemented on content-based image retrieval (CBIR) and classification problems for extracting similar images. In k-nearest neighbor (k-NN), the winning class is based on the k nearest neighbors determined by comparing the query image against all training samples. In this paper, a new nearest neighbor search (NNS) algorithm is proposed using a two-step process...