A new classification rule based on nearest neighbour search

2010 Ninth International Conference on Grid and Cloud Computing, 2010

kNN is a simple, but effective and powerful lazy learning algorithm. It has been now widely used in practice and plays an important role in pattern classification. However, how to choose an optimal value of k is still a challenge, which straightforwardly affects the performance of kNN. To alleviate this problem, in this paper we propose a new learning algorithm under the framework of kNN. The primary characteristic of our method is that it adopts mutual nearest neighbors, rather than k nearest neighbors, to determine the class labels of unknown instances. The advantage of mutual neighbors is that pseudo nearest neighbors can be identified and will not be taken into account during the prediction process. As a result, the final result is more reasonable. Experimental results conducted on UCI datasets show that the classification performance achieved by our proposed method is better than the traditional one.

In this paper, a new classification method is presented which uses clustering techniques to augment the performance of K-Nearest Neighbor algorithm. This new method is called Nearest Cluster approach, NC. In this algorithm the neighbor samples are automatically determined using clustering techniques. After partitioning the train set, the labels of cluster centers are determined. For specifying the class label of a new test sample, the class label of the nearest cluster prototype is used. Computationally, the NC method is faster than KNN, K 2 times. Also the clustering techniques lead to find the best number of neighbors based on the nature of feature space. The proposed method is evaluated on two standard data sets, SAHeart and Monk. Experimental results show the excellent improvement both in accuracy and time complexity in comparison with the KNN method.

k-nearest neighbor (k-NN) algorithm is one of the traditional methods that is used in classification. It assigns an unseen point to the dominant class between its k nearest neighbors within the data set. However, lack of a formal framework for selecting the number of the neighborhood k is problematic. This article investigates a novel method for calculating the optimum value of k using cross-validation techniques. The proposed method is fully automatic with no user-set parameters and it is also tested on different benchmark data set with comparison of other popular methods for selecting k. Keywords—K-fold cross-validation; optimum k; k-nearest neighbor; leave-one-out; pattern recognition

Spatial query which focus only on the geometrics properties of an object like points, rectangle etc. Now a day’s many new applications which involve the queries that completely aim to return an object which satisfy equally on spatial predicate and their associated text. Spatial query takes the given location and a keyword as the input and finds the object that matches the both spatial predicate and the text related to the given query. Some of the spatial queries are range search and nearest neighbor retrieval which includes only geometric properties of an object. This paper is about the related work involved for the efficient nearest neighbor search. Keywords- Spatial Query, Nearest Neighbor Search, IR2-Tree, Range Search, Spatial Predicate.

Nearest Neighbor Classifiers demand high computational resources i.e, time and memory. Two distinct methods are followed by researchers in Pattern Recogntion to reduce this computational burden. The first method is reducing the reference set or training set and the second method is dimensionality reduction which are referred as Prototype Selection and Feature Reduction(a.k.a Feature Extraction or Feature selection) respectively. In this paper, we cascaded the two methods to reduce the data set in both directions there by reducing the computational burden of Nearest Neighbor Classifier. The experiments are done on the bench mark datasets and the results obtained are satisfactory.

2011 IEEE Recent Advances in Intelligent Computational Systems, 2011

K-Nearest neighbor classifier (k-NNC) is simple to use and has little design time like finding k values in k-nearest neighbor classifier, hence these are suitable to work with dynamically varying data-sets. There exists some fundamental improvements over the basic k-NNC, like weighted k-nearest neighbors classifier (where weights to nearest neighbors are given based on linear interpolation), using artificially generated training set called bootstrapped training set, etc. These improvements are orthogonal to space reduction and classification time reduction techniques, hence can be coupled with any of them. The paper proposes another improvement to the basic k-NNC where the weights to nearest neighbors are given based on Gaussian distribution (instead of linear interpolation as done in weighted k-NNC) which is also independent of any space reduction and classification time reduction technique. We formally show that our proposed method is closely related to non-parametric density estimation using a Gaussian kernel.

2009

presented which uses clustering techniques to augment the performance of K-Nearest Neighbor algorithm. This new method is called Nearest Cluster approach, NC. In this algorithm the neighbor samples are automatically determined using clustering techniques. After partitioning the train set, the labels of cluster centers are determined. For specifying the class label of a new test sample, the class label of the nearest cluster prototype is used. Computationally, the NC method is faster than KNN, K 2 times. Also the clustering techniques lead to find the best number of neighbors based on the nature of feature space. The proposed method is evaluated on two standard data sets, SAHeart and Monk. Experimental results show the excellent improvement both in accuracy and time complexity in comparison with the KNN method.

2007

Abstract. Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier–classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such a problem these days with the computational power that is available.

2010

The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can't be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly classified into structure less and structure based techniques. In this paper, we present the survey of such techniques. Weighted kNN, Model based kNN, Condensed NN, Reduced NN, Generalized NN are structure less techniques whereas k-d tree, ball tree, Principal Axis Tree, Nearest Feature Line, Tunable NN, Orthogonal Search Tree are structure based algorithms developed on the basis of kNN. The structure less method overcome memory limitation and structure based techniques reduce the computational complexity.

This paper proposes SV-kNNC, a new algorithm for k-Nearest Neighbor (kNN). This algorithm consists of three steps. First, Support Vector Machines (SVMs) are applied to select some important training data. Then, k-mean clustering is used to assign the weight to each training instance. Finally, unseen examples are classified by kNN. Fourteen datasets from the UCI repository were used to evaluate the performance of this algorithm. SV-kNNC is compared with conventional kNN and kNN with two instance reduction techniques: CNN and ENN. The results show that our algorithm provides the best performance, both predictive accuracy and classification time.