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 …
Locality constrained representation-based K-nearest neighbor classification
K-nearest neighbor rule (KNN) is one of the most widely used methods in pattern
recognition. However, the KNN-based classification performance is severely affected by the …
recognition. However, the KNN-based classification performance is severely affected by the …
On kernel difference-weighted k-nearest neighbor classification
Nearest neighbor (NN) rule is one of the simplest and the most important methods in pattern
recognition. In this paper, we propose a kernel difference-weighted k-nearest neighbor (KDF …
recognition. In this paper, we propose a kernel difference-weighted k-nearest neighbor (KDF …
[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 …
A new general nearest neighbor classification based on the mutual neighborhood information
Z Pan, Y Wang, W Ku - Knowledge-Based Systems, 2017 - Elsevier
The nearest neighbor (NN) rule is effective for many applications in pattern classification,
such as the famous k-nearest neighbor (kNN) classifier. However, NN-based classifiers …
such as the famous k-nearest neighbor (kNN) classifier. However, NN-based classifiers …
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 …
[PDF][PDF] Effects of distance measure choice on knn classifier performance-a review
VS Prasatha, HAA Alfeilate, AB Hassanate… - arXiv preprint arXiv …, 2017 - academia.edu
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 …
Locally adaptive k parameter selection for nearest neighbor classifier: one nearest cluster
F Bulut, MF Amasyali - Pattern Analysis and Applications, 2017 - Springer
The k nearest neighbors (k-NN) classification technique has a worldly wide fame due to its
simplicity, effectiveness, and robustness. As a lazy learner, k-NN is a versatile algorithm and …
simplicity, effectiveness, and robustness. As a lazy learner, k-NN is a versatile algorithm and …
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 …
ENN: Extended nearest neighbor method for pattern recognition [research frontier]
This article introduces a new supervised classification method-the extended nearest
neighbor (ENN)-that predicts input patterns according to the maximum gain of intra-class …
neighbor (ENN)-that predicts input patterns according to the maximum gain of intra-class …