Nearest neighbor pattern classification

Cover, Thomas M.; Hart, P. D’Arcy

doi:10.1109/tit.1967.1053964

Cited by 12,289 publications

(5,695 citation statements)

References 4 publications

Supporting

Mentioning

5,561

Contrasting

Unclassified

123

Order By: Relevance

“…For the formulation of predictive models, a search for the optimal models was conducted by applying several machine learning techniques, including Multivariate Logistic Regression Analysis (MLRA), Artificial Neural Network (ANN), Decision Tree (DT) and Nearest Neighborhood (KNN) (27)(28)(29). These statistical methods have been described previously in the field of hepatic diseases [30][31][32].…”

Section: Development Of Predictive Models Using Data Mining Analysismentioning

confidence: 99%

FibroGENE: A gene-based model for staging liver fibrosis

Eslam

Hashem

Romero–Gómez

et al. 2016

Journal of Hepatology

View full text Add to dashboard Cite

: A gene-based model for staging liver fibrosis, Journal of Hepatology (2015), doi: http://dx.doi.org/10. 1016/j.jhep.2015.11.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Background& aim: The extent of liver fibrosis predicts long term outcomes, and hence impacts

show abstract

Section: Development Of Predictive Models Using Data Mining Analysismentioning

confidence: 99%

FibroGENE: A gene-based model for staging liver fibrosis

Eslam

Hashem

Romero–Gómez

et al. 2016

Journal of Hepatology

View full text Add to dashboard Cite

show abstract

“…Authors would like to express that no outlier has been artificially added into the problems. Three well-known machine learning algorithms, namely C4.5 [14], I-nearest neighbour (l-NN) [15] and SVM (Support Vector Machines) [16], have been tested with the original data sets, with the data sets after the application of OUTLIERoutF and once OUTLIERoutP has been carried out. We have used the implementations of the three aforementioned classifiers provided in WEKA tool [17] with the default parameters that are those recommended by the own authors of the algorithms when the corresponding code was released.…”

Section: Experimental Settingmentioning

confidence: 99%

Deleting or keeping outliers for classifier training?

Tallón‐Ballesteros

Riquelme

2014

2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)

View full text Add to dashboard Cite

show abstract

“…In a binary recognition setting, the algorithm is provided with two datasets: a reference dataset C 0 containing samples of one class only, and a mixed dataset C 1 containing an unlabelled collection of samples. The algorithm then exploits the asymptotic properties of NearestNeighbourhood classification on randomly selected reference sets (Cover and Hart, 1967). Specifically, it estimates the posterior probabilities Pr{C 0 |x} and Pr{C 1 |x} using an average number of times a point x is assigned to C 0 or C 1 with respect to a random reference set containing n points from each collection.…”

Section: Quasi-supervised Learningmentioning

confidence: 99%

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning

Onder¹,

Sarıoğlu²,

Karaçali³

2013

Micron

View full text Add to dashboard Cite

a b s t r a c tQuasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark classifier, suggest that quasi-supervised image texture labelling may be a useful method in the analysis and classification of pathological slides but further study is required to improve the results.

show abstract

Nearest neighbor pattern classification

Abstract: for some very helpful discussions, and Prof. D. E. Troxel for his help in designing the sensory display.

Cited by 12,289 publications

References 4 publications

FibroGENE: A gene-based model for staging liver fibrosis

FibroGENE: A gene-based model for staging liver fibrosis

Deleting or keeping outliers for classifier training?

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning

Contact Info

Product

Resources

About