Effects of distance measure choice on k-nearest neighbor classifier performance: a review
HA Abu Alfeilat, ABA Hassanat, O Lasassmeh… - Big data, 2019 - liebertpub.com
The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …
yet its performance competes with the most complex classifiers in the literature. The core of …
Distance and similarity measures effect on the performance of K-nearest neighbor classifier--a review
VB Prasath, HAA Alfeilat, A Hassanat… - arXiv preprint arXiv …, 2017 - arxiv.org
The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers,
yet its performance competes with the most complex classifiers in the literature. The core of …
yet its performance competes with the most complex classifiers in the literature. The core of …
A brief review of nearest neighbor algorithm for learning and classification
K Taunk, S De, S Verma… - … conference on intelligent …, 2019 - ieeexplore.ieee.org
k-Nearest Neighbor (kNN) algorithm is an effortless but productive machine learning
algorithm. It is effective for classification as well as regression. However, it is more widely …
algorithm. It is effective for classification as well as regression. However, it is more widely …
[HTML][HTML] Ensemble k-nearest neighbors based on centroid displacement
Abstract k-nearest neighbors (k-NN) is a well-known classification algorithm that is widely
used in different domains. Despite its simplicity, effectiveness and robustness, k-NN is …
used in different domains. Despite its simplicity, effectiveness and robustness, k-NN is …
Coarse to fine K nearest neighbor classifier
In this paper, we propose a coarse to fine K nearest neighbor (KNN) classifier (CFKNNC).
CFKNNC differs from the conventional KNN classifier (CKNNC) as follows: CFKNNC first …
CFKNNC differs from the conventional KNN classifier (CKNNC) as follows: CFKNNC first …
Hybrid -Nearest Neighbor Classifier
Conventional k-nearest neighbor (KNN) classification approaches have several limitations
when dealing with some problems caused by the special datasets, such as the sparse …
when dealing with some problems caused by the special datasets, such as the sparse …
[PDF][PDF] A new distance-weighted k-nearest neighbor classifier
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 …
using the dual distance-weighted function. The proposed DWKNN is motivated by the …
K-nearest neighbors rule combining prototype selection and local feature weighting for classification
X Zhang, H Xiao, R Gao, H Zhang, Y Wang - Knowledge-Based Systems, 2022 - Elsevier
Abstract K-Nearest Neighbors (KNN) rule is a simple yet powerful classification technique in
machine learning. Nevertheless, it suffers from some drawbacks such as high memory …
machine learning. Nevertheless, it suffers from some drawbacks such as high memory …
Reachable distance function for KNN classification
Distance function is a main metrics of measuring the affinity between two data points in
machine learning. Extant distance functions often provide unreachable distance values in …
machine learning. Extant distance functions often provide unreachable distance values in …
A new two-layer nearest neighbor selection method for kNN classifier
Y Wang, Z Pan, J Dong - Knowledge-Based Systems, 2022 - Elsevier
The k-nearest neighbor (kNN) classifier is a classical classification algorithm that has been
applied in many fields. However, the performance of the kNN classifier is limited by a simple …
applied in many fields. However, the performance of the kNN classifier is limited by a simple …