Open AccessJournal Article
A new distance-weighted k-nearest neighbor classifier
TLDR
The experiment results demonstrate that the proposed DWKNN is robust to different choices of k to some degree, and yields good performance with a larger optimal k, compared to the other state-of-art KNN-based methods.Abstract:
In this paper, we develop a novel Distance-weighted k -nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k -nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that our proposed classifier is robust to different choices of k to some degree, and yields good performance with a larger optimal k, compared to the other state-of-art KNN-based methods.read more
Citations
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Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background
TL;DR: In this article, the k-Nearest Neighbor (kNN) classification method has been used for economic forecasting in Iran and the results showed that kNN is more capable than other methods.
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A generalized mean distance-based k-nearest neighbor classifier
TL;DR: The experimental results demonstrate that the proposed GMDKNN performs better and has the less sensitiveness to k, which could be a promising method for pattern recognition in some expert and intelligence systems.
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Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach.
TL;DR: This paper proposes a CBR method that can be both executed automatically as an algorithm and presented visually in a user interface for providing visual explanations or for visual reasoning, and shows that the qualitative method has a classification accuracy comparable to k-Nearest Neighbors algorithms, but is better explainable.
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Breast Cancer Prediction using varying Parameters of Machine Learning Models
Puja Gupta,Shruti Garg +1 more
TL;DR: Six supervised machine learning algorithms such as k-Nearest Neighborhood, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine with radial basis function kernel, and Adam Gradient Descent Learning are presented.
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Improved pseudo nearest neighbor classification
TL;DR: The comprehensively experimental results suggest that the proposed LMPNN classifier is a promising algorithm in pattern recognition.
References
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Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
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Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties
Evelyn Fix,J. L. Hodges +1 more
TL;DR: In this paper, the discrimination problem is defined as follows: e random variable Z, of observed value z, is distributed over some space (say, p-dimensional) either according to distribution F, or according to Distribution G. The problem is to decide, on the basis of z, which of the two distributions Z has.
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The Distance-Weighted k-Nearest-Neighbor Rule
TL;DR: One such classification rule is described which makes use of a neighbor weighting function for the purpose of assigning a class to an unclassified sample.
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A local mean-based nonparametric classifier
TL;DR: A simple nonparametric classifier based on the local mean vectors is proposed that is compared with the 1-NN, k-nn, Euclidean distance, Parzen, and artificial neural network (ANN) classifiers in terms of the error rate on the unknown patterns.