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kNearest Neighbour Classifiers
, 2007
"... Abstract. Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach ..."
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Cited by 14 (0 self)
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Abstract. Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach
Extending the KNearest Neighbour Classification
"... This paper presents the extension of the distance weighted KNearest Neighbour (KNN) classification algorithm to SOs. The main novelties of the proposed extension are the use of a dissimilarity measure between SOs, the automated selection of K on the basis of crossvalidation, and the output of a sy ..."
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This paper presents the extension of the distance weighted KNearest Neighbour (KNN) classification algorithm to SOs. The main novelties of the proposed extension are the use of a dissimilarity measure between SOs, the automated selection of K on the basis of crossvalidation, and the output of a
fusion and KNearest Neighbour classification
"... Abstract—Landslide is induced by a wide range of ground movements, such as rock falls, deep failure of slopes, shallow debris flows, etc. These ground movements are caused when the stability of a slope changes from a stable to an unstable condition. A change in the stability of a slope can be caused ..."
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an opportunity for visualising the areas that require immediate attention from slope stability aspects. In this paper, À Trous algorithm based wavelet transform is used to merge IRS 1D LISSIII MSS (23.5 m) and PAN (5.8 m) images. The fused images are classified using Knearest neighbour for land cover
A Study of KNearest Neighbour as an
"... knearest neighbouralgorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data. 1 Introduction Data quality is a major concern in Machine Learning and other correlated areas such as Knowledge Discovery from Databases (KDD). As most Machine Learning algorithms induce know ..."
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knearest neighbouralgorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data. 1 Introduction Data quality is a major concern in Machine Learning and other correlated areas such as Knowledge Discovery from Databases (KDD). As most Machine Learning algorithms induce
Outlier detection using knearest neighbour graph
 United Kingdom
, 2004
"... We present an Outlier Detection using Indegree Number (ODIN) algorithm that utilizes knearest neighbour graph. Improvements to existing kNN distancebased method are also proposed. We compare the methods with real and synthetic datasets. The results show that the proposed method achieves resonable ..."
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Cited by 24 (0 self)
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We present an Outlier Detection using Indegree Number (ODIN) algorithm that utilizes knearest neighbour graph. Improvements to existing kNN distancebased method are also proposed. We compare the methods with real and synthetic datasets. The results show that the proposed method achieves resonable
A Study of KNearest Neighbour as an Imputation Method
 In HIS
, 2003
"... Data quality is a major concern in Machine Learning and other correlated areas such as Knowledge Discovery from Databases (KDD). As most Machine Learning algorithms induce knowledge strictly from data, the quality of the knowledge extracted is largely determined by the quality of the underlying data ..."
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Cited by 12 (0 self)
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into the knowledge induced. In this work, we analyse the use of the knearest neighbour as an imputation method. Imputation is a term that denotes a procedure that replaces the missing values in a data set by some plausible values. Our analysis indicates that missing data imputation based on the knearest neighbour
Connectivity of random knearest neighbour graphs
, 2006
"... Let P be a Poisson process of intensity one in a square Sn of area n. We construct a random geometric graph Gn,k by joining each point of P to its k = k(n) nearest neighbours. Recently, Xue and Kumar proved that if k ≤ 0.074 log n then the probability that Gn,k is connected tends to zero as n→∞, wh ..."
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Cited by 38 (7 self)
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Let P be a Poisson process of intensity one in a square Sn of area n. We construct a random geometric graph Gn,k by joining each point of P to its k = k(n) nearest neighbours. Recently, Xue and Kumar proved that if k ≤ 0.074 log n then the probability that Gn,k is connected tends to zero as n
Predicting and preventing student failure  using the knearest neighbour method to predict student performance in an . . .
"... and preventing student failure – using the knearest neighbour ..."
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Cited by 3 (0 self)
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and preventing student failure – using the knearest neighbour
Extensions of the k nearest neighbour methods for classification problems
 in Proc. of 26th IASTED International Conference on Artificial Intelligence and Applications, CD Proceedings ISBN: 9780889867109, 2008
"... The k Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a data ..."
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Cited by 3 (2 self)
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The k Nearest Neighbour (kNN) method is a widely used technique which has found several applications in clustering and classification. In this paper, we focus on classification problems and we propose modifications of the nearest neighbour method that exploit information from the structure of a
evaluated. Support Vector Machine and K Nearest Neighbours
"... Abstract This paper examined the application of Machine Learning techniques for famine prediction. Early detection of famine reduces vulnerability of the society at risk. The dataset used in the study was collected between 2004 to 2005 across households in the different regions of Uganda. Dataset f ..."
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from the northern region was found to be most suitable to training datasets of other regions. Classification performance of four methods as Support Vector Machine, K Nearest Neighbours, Naïve Bayes and Decision tree in prediction of famine were
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