nearest neighbor classification method
EFFICIENT CLASSIFICATION METHOD FOR LARGE DATASET BY ASSIGNING THE KEY VALUE IN CLUSTERING
... the classification for large dataset and Canberra distance is generalized so that it can process the data with categorical ...the nearest neighbor classification is ...proposed ...
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Exact fuzzy k Nearest neighbor classification for big datasets
... on classification problems, the method proposed in [10] is an approximate kNN algorithm consisted of two ...the nearest neighbors and reports the final k nearest ...
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Quality, frequency and similarity based fuzzy nearest neighbor classification
... Although FNN performs well, many authors have tried to improve it further. The strategy that we focus on in this paper is to improve FNN using fuzzy rough set theory [4], [5]. Some previous efforts to do this have been ...
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Dimensionality reduction by minimizing nearest-neighbor classification error
... The proposed approach is also related to the family of pattern recog- nition algorithms based on gradient descent optimization, in particular, the neural networks algorithms Bishop (1995) such as the Multi Layer ...
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Classification with the nearest neighbor rule in general finite dimensional spaces
... others). Classification and Regression Tree is another intuitive standard method that relies on a recursive dyadic partition of the state space, introduced in [BFOS84] and greatly improved by an averaging ...
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Parametric local metric learning for nearest neighbor classification
... Adaptive Nearest Neighbor classification [8], DANN, learns local metrics by shrinking neighborhoods in directions orthogonal to the local decision bound- aries and enlarging the neighborhoods ...
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On the selection of the globally optimal prototype subset for Nearest-Neighbor classification
... We propose a method in which a set of prototypes of pre-specified cardinality p is sought, minimizing an empirical misclassification cost. As prototype design, we assume that prototypes are to be chosen from a given ...
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EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data
... each method, differentiating between build- ing and classification ...the classification time involves the average time spent to label a test ...The classification time of EPRENNID is slightly ...
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Nearest Neighbor Search with Anytime Clustering Method
... document classification. To solve this problem, a method for finding the nearest neighbors of a given data point is needed and mainly it depends upon clustering ...(Online ...
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Identification Of Speaker Recognition For Audio Forensic Using K-Nearest Neighbor
... Coefficient method, MFCC is a method used in the introduction of speakers to describe the characteristics of a signal, which has a relationship to the characteristics of the speaker vocal channel so that it ...
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Exact fuzzy k-Nearest neighbor classification for big datasets
... on classification problems, the method proposed in [10] is an approximate kNN algorithm consisted of two ...the nearest neighbors and reports the final k nearest ...
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Title: PERFORMANCE ANALYSIS OF HYBRID APPROACH OF K-NN ALGORITHM USING MULTIPLE-LEVEL LEARNING FOR TEXT CLASSIFICATION
... learning method and widely used in similarity ...k nearest patterns in the training database, and uses the k pattern to determine the class of pattern ...
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LZW Compressed Text Classification using Nearest Neighbor Classifier
... Abstract- Internet is a pool of information, which contains billions of text documents which are stored in compressed format. In literature we can find many text classification algorithms which work on ...
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Predicting Customers Churn in Telecom Industry using Centroid Oversampling Method and KNN Classifier
... the classification performance of imbalanced data learning, a novel over-sampling method, Centroid Oversampling Technique, based on centroid of three nearest neighbor points, is ...
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Nearest Neighbor Classification with Locally Weighted Distance for Imbalanced Data
... If there are significant differences between the algorithms, we used post-hoc test [27] to find out which algorithms actually differed. In this paper, an improved Friedman test proposed by Iman and Davenport [28] is ...
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
... LMNN classification can be used for more effective dimensionality reduction in ball ...kNN classification from both these methods on the MNIST data set of handwritten ...particular method of ...
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The Mutual Nearest Neighbor Method in Functional Nonparametric Regression
... k-nearest neighbor (kNN) is one of the most famous nonparametric ...mutual nearest neighbors (MNN) method is a variant of kNN ...mutual nearest neighbors rather than k-nearest ...
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Comparative Analysis of K-Nearest Neighbor and Modified K-Nearest Neighbor Algorithm for Data Classification
... validation and it may be bigger if k-fold is larger depending on requirement [7], however according to Last (2006) there is no exact terms on determining how many k-fold is needed, it may be 33%, 50%, and 60% of the data ...
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A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule
... it is impractical to calculate these probabilities in advance and then store them. There is therefore not a training process for DMLkNN. The probabil- ities are computed locally for each instance to be classified. ...
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Data Classification According to the Genetic Binary Tree Based on the Nearest Neighbor
... this method. At first, we consider 5 of the nearest distances and specify the values of their ...this method, we use the similarity of test data with training data to achieve a better estimation of ...
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