K-nearest neighbours
Sleep Heart Rate Variability Analysis and k-Nearest Neighbours Classification of Primary Insomnia
... of k-nearest neighbours and distance metrics (Hu, Huang, Ke, & Tsai, ...of k is determined heuristically by inspecting the data and ...of k improves the overall classifier ...
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Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions
... their k nearest neighbours are calculated and used to de- fine a unique threshold to decide if a test sample is inside or outside the model’s AD (for example, 95th ...the nearest neighbour ...
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K-Nearest Neighbours (K-NN) Approach Based on Network Summarization
... the K-shortest route that summarize the ...of K-subsets of shortest path in a spatial network. Nearest neighbour (NN) query is one of the most significant operations in spatial databases and their ...
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Classification of brain tumor using devernay sub-pixel edge detection and k-nearest neighbours methodology
... Any disease can be treated only once it is imaged, detected and classified. This paper proposes a set of algorithms for classification of a brain tumor with better accuracy and efficiency. The proposal uses a JPEG format ...
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Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data
... and K-nearest neighbours and gradient boosting decision trees) and 3 deep learning architecture (including convolutional neural network (CNN), long-short term network (LSTM) and Gated Recurrent Unit ...
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“Noisy beets”: impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris
... (K-nearest neighbours—KNN, random forest—RF, ridge logistic regression—LR, and support vector machines with linear or radial basis function kernels) was ...
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Evaluation of prediction models for the staging of prostate cancer
... Background: There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the ...
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Machine learning based detection of Kepler objects of interest
... For our experiments we adopted the use of five different classification approaches. These were primarily selected on the basis of their widespread use, well understood behaviour, and promising performance in a variety of ...
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The k NN algorithm for compositional data: a revised approach with and without zero values present
... In compositional data, an observation is a vector with non-negative components which sum to a constant, typically 1. Data of this type arise in many areas, such as geology, archaeology, biology, economics and political ...
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Fraud detection models and payment transactions analysis using machine learning
... (SVM), K-Nearest neighbours, Decision Tree Classifier and Artificial Neural Networks; provide the notion of how these methods operate the data and yield the ...
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Road Detection Using Classification Algorithms
... This paper presents a road detection method based on classification algorithms. Proposed method consists of two phases. In the first phase, a binary image is obtained by utilizing greyscale transformation and ...
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Title : Analysis of Machine Learning Approaches to Detect and Classify Breast Cancer Author (s) : M. Lakshmitha , A. Abdul Hayum
... This paper focuses on classification of breast cancer as benign or malignant. For the classification of images there are some algorithms such as K-nearest neighbours, Support Vector Machines (SVM), ...
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Disease Prediction with Machine Learning Algorithm
... Abstract: The new Medical Health care & Management System with web application is designed to resolve the issues related to the management of health check up, appointments, reports. In order to manage the Health ...
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Nearest neighbours in least squares data imputation algorithms with different missing patterns
... and Mirkin, Boris 2006 Nearest neighbours in least-squares data imputation algorithms with different missing patterns.. Computational Statistics & Data Analysis 50 4, pp.[r] ...
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What Affects K Value Selection In K-Nearest Neighbor
... best k value data of 2-class data sets is then compared to number of instance, attribute type, and class distribution (Figures 1 – ...best k value data of n-class data sets is compared to number of instance ...
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An Investigation of Performance Analysis of Anomaly Detection Techniques for Big Data in SCADA Systems
... on nearest neighbours, clustering and statistical approaches and investigate the performance analysis of these techniques in critical infrastructure applications such as SCADA ...
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Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform
... Objectives. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. Material and methods. The design phase of the classification model comprises the following stages: ...
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Combining Syntactic Co occurrences and Nearest Neighbours in Distributional Methods to Remedy Data Sparseness
... their nearest neighbours we could expand them with the syntactic co-occurrences of their nearest neighbours to arrive at more uniform ...
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Detecting Compositionality of Multi Word Expressions using Nearest Neighbours in Vector Space Models
... the neighbours have to be ...WordNet neighbours of eat (for all senses of eat), we count the co-occurrences with hat in a given window size and rank them accord- ...ranked neighbours of apple for eat ...
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HSQC spectral based similarity matching of compounds using nearest neighbours and a fast discrete genetic algorithm
... a nearest neighbour approach and a probabilistic method based on an existing discrete genetic ...The nearest neighbour and genetic algorithm-based methods ranked highly particular structures missed by ...
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