K-NN classification algorithm
Research of liver cancer detection based on improved K NN algorithm
... To remove features with little effect on classification, we introduced the PCA into the process of classification of K-NN algorithm. The goal of PCA is to identify the most meaningful ...
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An Hybrid Intrusion Detection Approach based on SVM Classification and k NN
... however don't give their sorts. As a matter of fact, correct intrusion data is essential for arrange executives to take applicable security activities. The second restriction is low detection execution for low- ...
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Segmentation of Lung Nodule Image Using Global Thresholding and Classification by K-NN
... image.The K-nearest neighbour’s algorithm is a method forclassifying objects based on closest training ...integer k is chosen K-Nearest Neighbours (KNN) classification divides data into ...
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A benchmark of classification framework for non communicable disease prediction: A review
... The k-nearest neighbor (k-nn) model is a well- known supervised learning algorithm for pattern recognition that first introduced by Fix and Hodges (1951), and is still one of the most popular ...
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Comparing pixel-based and object-based algorithms for classifying land use of arid basins (Case study: Mokhtaran Basin, Iran)
... object-oriented classification where the focus was found on the difference between classification accuracies (Dean, ...the K-Nearest Neighbor (K-NN) and indicated that the overall ...
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An Efficient Mining Model For Enhancing Text Classification Using k NN
... the algorithm is easy to implement by computing the distances from the test sample to all stored vectors, but it is computationally intensive, especially when the size of the training set ...search ...
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With an efficient time series datasets classification fast accuracy model for dynamic data sets using classical-k-nn algorithm
... Neighbour Algorithm with the relatively expensive Dynamic Time Warping as the distance measure, successful deployments on resource constrained devices remain ...Neighbor Algorithm is without inheriting its ...
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PRIVACY PRESERVING TECHNIQUE USING K-NN CLASSIFICATION IN OUTSOURCES ENVIRONMENT
... We use this algorithm for maintaining privacy policies. The overall structure of AES encryption/decryption is shown below. The number of rounds is for the case when the encryption key is 128 bit long. (As ...
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A Study on Content Based Image Retrieval System using K-NN Algorithm and Mobile Agents
... basic k-NN algorithm and some of its variations, such as weighted k-NN and assigning weights to objects, are relatively well known, some of the more advanced techniques for ...
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Multiple Classifiers System for Medical Diagnosis
... network, K-NN from Case based Reasoning and modified towing splitting rule in Classification Tree ) in the market, but the main problem is not a single machine learning algorithm has all the ...
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Color Based Image Segmentation using Classification of K Nn With Contour Analysis Method
... ABSTRACT Recent advances in computer technology have made it possible to create database for large number of images. A major approach directed towards achieving CBIR is the use of low-level visual features of the image ...
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Dynamic k NN with Attribute Weighting for Automatic Web Page Classification(Dk NNwAW)
... its classification results can be greatly improved by further research into some of its most basic areas, such as the neighborhood size, use of validity, and the clustering ...dynamic k-NN for ...
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Multi-Agent Based Diagnostic Model for Breast Tumour Classification
... In this study, the researchers have proposed a multi-agent platform in a distributed data mining system for diagnosing breast tumours with the aid of k-NN algorithm. The researchers introduced three ...
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Pedagogy And Reduction Of K-nn Algorithm For Filtering Samples In The Breast Cancer Treatment
... The primary focus of the usage of the classification mechanism is the length that appears in collateral degrees either with data reduction stage or without data reduction stage. As the distance is being dominated ...
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Content Based Image Retrieval using Wavelet based MultiResolution Analysis
... Content Based Image Retrieval (CBIR) is an emerging area of engineering application focusing on algorithms and methods to extract image features from a query image and retrieve similar images from large archives. It has ...
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Detection and Classification of Apple Fruit Disease using K-NN Classification and GLCM Features
... segmentation. K-Means clustering algorithm was developed by ...(1967). K-means is generally used to determine the natural groupings of pixels present in an ...into k clusters in which one ...
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AN ENHANCED APPROACH FOR OUTLIER DETECTION AND CLASSIFICATION IN CATEGORICAL DATA USING CLASSIC K NN ALGORITHM
... This research described clustering by categorical attributes is a challenging task in data mining applications. Unlike numerical attributes, it is difficult to define a distance between pairs of values of a categorical ...
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Machine Learning using Exploratory Analysis to Predict Taxi Fare
... In classification problems, the dependent variable is ...and classification problems. The K-NN algorithm is a simple, easy-to-implement supervised machine learning algorithm that ...
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Application Of Dimensionality Reduction On Classification Of Colon Cancer Using Ica And K-Nn Algorithm
... using k-Nearest Neighbor algorithm to classify colon cancer dataset which contains DNA microarray gene expression data with 2000 features and 62 ...the classification performance in terms of ...
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Title: An Enhanced Model for the Classification of Mined Data
... Text classification is the processing of classifying documents into pre-defined categories based on their ...and classification of textual ...both K-Nearest Neighbour (KNN) Algorithm and ...
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