Fuzzy Monotonic <i>K</i>-Nearest Neighbor Versus Monotonic Fuzzy <i>K</i>-Nearest Neighbor

Hong, Zhu; Wang, Xizhao; Wang, Ran

doi:10.1109/tfuzz.2021.3117450

Cited by 9 publications

(3 citation statements)

References 46 publications

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“…The k-nearest neighbor (KNN) classifier is one of the well-known instance-based classifiers because of being easy to implement and has a low error bounded by twice the Bayes error [1]. After decades of development, the KNN classifier has various variations, such as the local mean-based k-nearest neighbor (LM-KNN) [2], the multi-local meansbased k-harmonic nearest neighbor (MLM-KHNN) [3] and the fuzzy k-nearest neighbor (FKNN) [4].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A Heuristic Hybrid Instance Reduction Approach Based on Adaptive Relative Distance and k-means clustering

Liu

2023

Preprint

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The k nearest neighbor (KNN) classifier is one of the well-known instance-based classifiers. Nevertheless, the low efficiency in both running time and memory usage is a great challenge in the KNN classifier and its improvements due to noise and redundant samples. Although hybrid instance reduction approaches have been postulated as a good solution, they have the following issues: (a) adopted edition methods in existing hybrid instance reduction approaches are susceptible to harmful samples around the tested sample; (b) existing hybrid instance reduction approaches retain many internal samples, which contributes little to the classification accuracy and (or) leading to the low reduction rate; (c) existing hybrid instance reduction approaches rely on too many parameters. The chief contributions of this article are that (a) a novel heuristic hybrid instance reduction approach based on adaptive relative distance and k-means clustering (HIRRDKM) is proposed against the above issues; (b) a novel concept, i.e., the adaptive relative distance, is first proposed and calculated for each sample; (c) a novel edition method based on adaptive relative distance (EMARD) in HIRRDKM is second proposed to filter out harmful samples; (d) a novel condensing method based on adaptive relative distance and k-means clustering (CMRDKM) in HIRRDKM is third proposed to obtain condensed borderline samples from the training set without harmful samples. Experiments have proved that (a) HIRRDKM outperforms 6 state-of-the-art hybrid instance reduction methods on real data sets from various fields in weighing reduction rate and classification accuracy of KNN-based classifiers; (b) the running time of HIRRDKM is competitive.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: A Heuristic Hybrid Instance Reduction Approach Based on Adaptive Relative Distance and k-means clustering

Liu

2023

Preprint

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“…where equation (1) indicates that the sum of all demands of all customer nodes served by one vehicle should not be greater than the total carrying capacity of the vehicle; equation (2) indicates the calculated relationship between the moment when the vehicle arrives at the customer point and the moment when it leaves the customer point; equation (3) indicates the time from point i to point j; equation (4) indicates to be earlier than the latest delivery time; equation (5) indicates the elimination of subloops; and equation (6) indicates that each customer point is served and is served only once. The moment when the last customer is delivered in the line…”

Section: Objective Modelmentioning

confidence: 99%

“…The nearest neighbor algorithm [1]is a combinatorial optimization algorithm that is often mentioned in the study of distribution vehicle path optimization problems. For example, Zhu et al [2] proposed fuzzy monotonic K-nearest neighbors to construct monotonic classifiers by exploiting fuzzy dominance relations between pairs of instances and using fuzzy dominance relations between non-comparable instances, and finally experimentally verified that FMKNN has advantages over other state-of-the-art monotonic classifiers.…”

Section: Introductionmentioning

confidence: 99%

Pharmaceutical distribution path optimization based on K-nearest neighbor algorithm

Yang

2023

IJBLS

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With the changing situation of the prevention and control of the new crown epidemic, the demand for various types of pharmaceutical supplies is rising, which puts forward very high requirements on the timeliness of distribution. In view of this, this paper establishes a pharmaceutical logistics distribution path optimization model by comprehensively considering constraints such as vehicle loading and latest delivery time in pharmaceutical distribution. In order to solve this type of problem, a nearest neighbor algorithm is proposed, which introduces correlation coefficients to realize the improvement of the performance of the original algorithm. At the end of the paper, the effectiveness of the model and the algorithm is verified by applying a comparative analysis with the existing correlation algorithm with real data from a pharmaceutical company. The experimental results show that the method effectively reduces the delivery mileage and improves the pharmaceutical delivery timeliness.

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A heuristic hybrid instance reduction approach based on adaptive relative distance and k-means clustering

Li,

Zhao,

Liu

2024

J Supercomput

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Fuzzy Monotonic K-Nearest Neighbor Versus Monotonic Fuzzy K-Nearest Neighbor

Cited by 9 publications

References 46 publications

RETRACTED: A Heuristic Hybrid Instance Reduction Approach Based on Adaptive Relative Distance and k-means clustering

RETRACTED: A Heuristic Hybrid Instance Reduction Approach Based on Adaptive Relative Distance and k-means clustering

Pharmaceutical distribution path optimization based on K-nearest neighbor algorithm

A heuristic hybrid instance reduction approach based on adaptive relative distance and k-means clustering

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