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751
2 Per Jönsson and Claes Wohlin Benchmarking kNearest Neighbour Imputation with Homogeneous Likert Data
"... Abstract. Missing data are common in surveys regardless of research field, undermining statistical analyses and biasing results. One solution is to use an imputation method, which recovers missing data by estimating replacement values. Previously, we have evaluated the hotdeck kNearest Neighbour ( ..."
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Abstract. Missing data are common in surveys regardless of research field, undermining statistical analyses and biasing results. One solution is to use an imputation method, which recovers missing data by estimating replacement values. Previously, we have evaluated the hotdeck kNearest Neighbour
www.elsevier.com/locate/csda Convergence of random knearestneighbour imputation
, 2006
"... Random knearestneighbour (RKNN) imputation is an established algorithm for filling in missing values in data sets. Assume that data are missing in a random way, so that missingness is independent of unobserved values (MAR), and assume there is a minimum positive probability of a response vector be ..."
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Random knearestneighbour (RKNN) imputation is an established algorithm for filling in missing values in data sets. Assume that data are missing in a random way, so that missingness is independent of unobserved values (MAR), and assume there is a minimum positive probability of a response vector
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
The use of the area under the ROC curve in the evaluation of machine learning algorithms
 PATTERN RECOGNITION
, 1997
"... In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multilayer Perceptron, kNe ..."
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Cited by 685 (3 self)
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In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multilayer Perceptron, kNearest
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
A Friendly Statistics Package for Microarray Analysis
"... Summary: The friendly statistics package for microarray analysis (FSPMA) is a tool that aims to fill the gap between simple to use and powerful analysis. FSPMA is a platformindependent Rpackage that allows efficient exploration of microarray data without the need for computer programming. Analysi ..."
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. Analysis is based on a mixed model ANOVA library (YASMA) that was extended to allow more flexible comparisons and other useful operations like k nearest neighbour imputing and spikebased normalisation. Processing is controlled by a definition file that specifies all the steps necessary to derive analysis
KNearest Neighbours based on Mutual Information for Incomplete Data Classification
"... Abstract. Incomplete data is a common drawback that machine learning techniques need to deal with when solving reallife classification tasks. One of the most popular procedures for solving this kind of problems is the Knearest neighbours (KNN) algorithm. In this paper, we present a weighted KNN ap ..."
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Abstract. Incomplete data is a common drawback that machine learning techniques need to deal with when solving reallife classification tasks. One of the most popular procedures for solving this kind of problems is the Knearest neighbours (KNN) algorithm. In this paper, we present a weighted KNN
CFGeNe: Fuzzy Framework for Robust Gene Regulatory Network Inference
"... Abstract — Most Gene Regulatory Network (GRN) studies ignore the impact of the noisy nature of gene expression data despite its significant influence upon inferred results. This paper presents an innovative CollateralFuzzy Gene Regulatory Network Reconstruction (CFGeNe) framework for Gene Regulato ..."
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values, compared to other data imputation methods including: Least Square Impute (LSImpute), KNearest Neighbour Impute (KNN), Bayesian Principal Component Analysis Impute (BPCA) and ZeroImpute. The statistical significance of this improved performance has been underscored by gene selection and also
Balanced kNearest Neighbor Imputation
"... In order to overcome the problem of item nonresponse, random imputations are often used because they tend to preserve the distribution of the imputed variable. Among the methods of random imputation, the random hotdeck has the interesting property that the imputed values are observed values. We pre ..."
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In order to overcome the problem of item nonresponse, random imputations are often used because they tend to preserve the distribution of the imputed variable. Among the methods of random imputation, the random hotdeck has the interesting property that the imputed values are observed values. We
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
Results 1  10
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751