K-nearest neighbor classifier
FINkNN : A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples
... This work introduces FINkNN, a k-nearest-neighbor classifier operating over the metric lattice of conventional interval-supported convex fuzzy sets. We show that for problems involving ...
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Pruned fuzzy K nearest neighbor classifier for beat classification
... Simple K-Nearest Neighbor (SKNN) classifier used in our previous work offers many advantages over other classifiers including simplicity and ease of parallel implementation, adaptability and ...
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Evaluating the Performance of Dual Weighted K Nearest Neighbor Classifier
... Dynamic K-Nearest Neighbors Naive Bayes with Attribute Weighted method to improve KNN's accuracy was developed by ...the classifier. The best value of „k' is learned eagerly at training time ...
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Theoretical Analysis of Cross-Validation for Estimating the Risk of the $k$-Nearest Neighbor Classifier
... This new strategy relies on several steps. First exploiting the connection between the LpO estimator and U-statistics (Koroljuk and Borovskich, 1994) and the Rosenthal inequal- ity (Ibragimov and Sharakhmetov, 2002), we ...
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Efficient Prediction and Classification of Epileptic Seizures Using EEG Data Based on Univariate Linear Features
... using k-nearest neighbor classifier with different values of k from 1 to ...20. K-nearest neighbor classifier gives maximum accuracy and true positive rate ...
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A Review on Plant Texture Analysis Using Leaf Images
... a nearest neighbour classifier (1NN), a k nearest neighbor classifier (k NN) and a radial basis probabilistic neural network (RBPNN) are used, based on runlength ...
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Crab Gender Classification Using Image Processing, Fuzzy Logic And K Nearest Neighbor (KNN) Classifier
... Abstract: The study aims to determine the gender of a crab with the use of image processing, fuzzy logic, and k-nearest neighbor classifier. The determination of crab gender is usually ...
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A Novel Weighted Voting for K-Nearest Neighbor Rule
... sub-optimal k is greatly affected by the finite sample space in the practical ...if k is too small, the classification result of the query is sensitive to the data sparseness and the noisy, ambiguous or ...
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Automatic Detection of Whitefly Pest using Statistical Feature Extraction and Image Classification Methods
... Then, k-means clustering method is used for segmenting pest from infected ...Bayesian classifier, Binary decision tree classifier and k-Nearest neighbor classifier are ...
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Detection & Classification of Brain Tumour
... ABSTRACT: The use of digital images has become a subject of widespread interest in different areas such as medical technological application and many others. There are lots of examples where image processing helps to ...
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Evaluating Performance Measures Using Hybrid Models With Fuzzy Finite State Machine For Human Activity Recognition
... The k-nearest neighbor classifier is then used as the fitness function in the particle swarm optimization to evaluate the performance of the feature subset, that is, feature combination with ...
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Breast Cancer Computer-aided Diagnosis System from Digital Mammograms
... (K-Nearest Neighbor) classifier, “SVM (Support Vector Machine) classifier, LDA (Linear Discriminant) classifier, QDA (Quadtradic Discriminant Analysis) classifier, NB ...
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What Affects K Value Selection In K-Nearest Neighbor
... K-NEAREST NEIGHBOR classifier is a popular classifier that has been applied in many fields, such as in text categorization [2], intrusion detection [3], handwritten digit recognition ...
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Normal/Abnormal Gait Analysis based on the Statistical Registration and Modeling of the Frontal View Gait Data
... apply K-nearest- neighbor classifier, using the estimated parameters, to perform normal/abnormal gait detect, and present results from an experiment involving 120 subjects (young person), and ...
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Secure K-Nearest Neighbor Search By Keywords
... Information Mining has wide applications in numerous territories, for example, saving money, prescription, experimental examination and among government organizations. Grouping is one of the regularly utilized ...
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Text Classification and Classifiers: A Comparative Study
... In current research automatic classification of documents into predefined categories has observed as an active attention, the documents can be classified in three ways, unsupervised, supervised and semi supervised ...
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An Experimental Analysis of Various Data Mining Techniques for Software Bug Classification
... Proceeding further Menzies et al. [7] offered a new approach SEVERIS(SEVERity ISsue assesment) for assigning the different levels of severity to bug reports. . Then Lamkanfi Ahmed et al. [9] predicted and verified the ...
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LZW Compressed Text Classification using Nearest Neighbor Classifier
... After giving an effective representation for a document, the task of text classification is to classify the documents to the predefined categories. In order to do so, many statistical and computational models have been ...
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Real time Processor for the Authentication of Affected Herbal Medicinal Leaves Using Image Processing Techniques- Review
... the classifier that used Probabilistic Neural Network ...hybrid(composite) classifier by combining two classifiers in common use— classification trees and k- nearest-neighbor ...the ...
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Supervised Learning Classification Algorithms Comparison
... Abstract : Under supervised machine learning, classification tasks are one of the most important tasks as a part of data analysis. It gives a lot of actionable insights to data scientists after using different machine ...
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