k-nearest neighbour feature classification
Improving classification performance of k nearest neighbour by hybrid clustering and feature selection for non communicable disease prediction
... hybrid k-means as clustering technique, Weight SVM as feature selection technique and k-nearest neighbour as classifier ...that k-means + weight by SVM + k-nn improved the ...
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Prediction Of Phishing Websites And Analysis Of Various Classification Techniques
... a classification model using Bayesian statistical classification, J48 Decision tree, Random Forest, K-Nearest Neighbour (KNN) and ...greater classification accuracy than the ...
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K Nearest Neighbor Classification Approach for Face and Fingerprint at Feature Level Fusion
... at feature extraction level for face and ...at feature extraction level by first making the feature sets compatible for concatenation and then reducing the feature sets to handle the “problem ...
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A Study on Content Based Image Retrieval System using K-NN Algorithm and Mobile Agents
... that K-NN keeps all the training ...especially K-NN – make decision based on the entire training data set (in the best case a subset of ...the k-nearest neighbour algorithm ...
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Heart Disease Prediction System Using K Nearest Neighbour Classification Technique
... The heart is an operating system of the human body. If it does not function properly it will affect other parts also. Heart disease problem describes a range of conditions that affect the heart. The existing system uses ...
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Enhancing the Classification Accuracy of Noisy Dataset By Fusing Correlation Based Feature Selection with K-Nearest Neighbour
... The performance of our proposed approach has been tested with 10 different types of dataset with no or minimal level of missing values in each dataset. Each of the dataset and their percentage of corresponding missing ...
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A Review of Data Classification Using K-Nearest Neighbour Algorithm
... for K-Near neighbor ...has k nearest examples where feature space and majority of them are having the same label “y”, then “x” belongs to ...The K-NN method is mostly depends upon ...
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Storm Identification in the Rainfall Data Using Singular Value Decomposition and K-Nearest Neighbour Classification
... and classification methods have adopted term-based approaches suffered from the problems of feature evolution and concept ...using K Nearest ...f-measure, K-NN, Precision, Recall ...
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An Enhance Fingerprints Recognition System Using Haralick Features
... for feature extraction and K-Nearest Neighbour Rule using Euclidean Distance used for template ...process. K-NN algorithm also used tofind out the scalability problem and the accuracy ...
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Probabilistic Classification from a K Nearest Neighbour Classifier
... per feature can be combined to produce various class ...sample feature combinations, can thus be evaluated like-for-like, (allowing a selection of the preferred classification ...
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Title: An Enhanced Model for the Classification of Mined Data
... the feature of the sentence, the K-Nearest Neighbour and Euclidean Distance Classifier has been proposed, each sentence can be represented as a vector Si = (wi1;wi2; :::;wim) of features (the ...
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Blur Classification and Deblurring of Images
... the k closest training examples in the feature ...whether K-NN is used for classification or regression. In k-NN classification, the output is a class ...its k ...
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Sales Forecasting using Linear Regression and K-Nearest Neighbour
... and K-NN along-with Euclidean Distance Function thereafter, features and variant factors will drawn with results based on categories, products, cities and countries which will proposed various escalating and ...
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Discretisation of Data in a Binary Neural k-Nearest Neighbour Algorithm
... for classification such as MLP or RBF, (Bishop, 1995) require repeated training epochs and the resultant network is effectively a black ...binary k-NN described in the following ...
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Towards Detecting Deception using K Nearest Neighbour Model
... Security over the years remains a major concern of all especially the law enforcement agencies. One way of arresting this concern is to be able to reliably detecting deception. Detecting deception remains a difficult ...
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Benchmarking Classification Models for Emotion Recognition in Natural Speech: a Multi-Corporal Study
... From the results it can be seen that classifying evaluation is more difficult than classifying activation or power—the results of classification on datasets UUDB-EVAL and VAM- EVAL are considerably worse than the ...
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Introduction to k Nearest Neighbour Classification and Condensed Nearest Neighbour Data Reduction
... their classification into account when classifying the new ...the nearest points. This latter method is what we consider now in the k Nearest Neighbours ...
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Analysis of diabetes mellitus for early prediction using optimal features selection
... The point of this examination is to the finding of diabetes illness, which is a stand- out amongst the most vital infections in the restorative field utilizing Generalized Dis- criminant Analysis (GDA) and Least Square ...
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Financial Analysis using Data Mining
... algorithms: K Nearest Neighbour, Rule Based Classification and Deep Learning for predicting whether the NASDAQ market will increase every month or not and with higher ...
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A Review on Plant Texture Analysis Using Leaf Images
... or classification system can use different characteristics of the flora, starting at very simple level such as: shape and colour of the leaf, flower and fruit type, branching style, root type, seasonality, ...
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