Validation Based Modified K-Nearest Neighbor

Abstract

Articles you may be interested inFinger vein identification using fuzzy-based k-nearest centroid neighbor classifier AIP Conf. Proc. 1643, 649 (2015); 10.1063/1.4907507Breast cancer classification using cluster k-nearest neighbor AIP Conf.A generalized K-nearest neighbor decision rule for isolated word recognition Abstract. In this paper, a new classification method for enhancing the performance of K-Nearest Neighbor is proposed which uses robust neighbors in training data. The robust neighbors are detected using a vahdation process. This method is more robust than traditional equivalent methods. This new classification method is called Modified K-Nearest Neighbor. Inspired the traditional KNN algorithm, the main idea is classifying the test samples according to their neighbor tags. This method is a kind of weighted KNN so that these weights are determined using a different procedure. The procedure computes the fraction of the same labeled neighbors to the total number of neighbors. The proposed method is evaluated on a variety of several standard UCI data sets. Experiments show the excellent improvement in accuracy in comparison with KNN method.

Multiple k-Nearest Neighbor Classifier and Its Application to Tissue Characterization of Coronary Plaque

“…Parvin et al [20] proposed the modified k-nearest neighbor (MKNN) method so far. This method has a similar idea to our proposed MkNN classifier on the point that it utilizes the information of the neighborhood of POI.…”

Novel Text Categorization by Amalgamation of Augmented K-Nearest Neighbourhoodclassification and K-Medoids Clustering

“…Automatic classification of data points in training set based on k value was a research finding of G Guo.In 2003 SC Baguai and K Pal assigned rank to training data for each category by using Gaussian distribution, thus it dominated the other variations of kNN [9]. Modified kNN [10] suggested by Parvin et.al in 2008 used weights and validity of data points to classify nearest neighbor and used various outlier elimination techniques. In the year, 2009 Zeng et.al planned a novel idea for kNN by using n-1 classes to the entire training to address the computational complexity [11].…”

3N-Q: Natural Nearest Neighbor with Quality

“…Inspired by the neighbor concept (Parvin, Alizadeh, & Minaei-Bidoli, 2008), we suppose that the quality of a sample is determined by its neighbors. Hence, we provide the following formula to compute the quality.…”