A New Weighted k-Nearest Neighbor Algorithm Based on Newton’s Gravitational Force

Aguilera, Juan Carlos; González, Luis; Montes-y-Gómez, Manuel; Rosso, Paolo

doi:10.1007/978-3-030-13469-3_36

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Usually, the coordinates of the first K reference points are selected. e distance weighted K-Nearest Neighbor (DW-KNN) is based on the KNN [30], where the K nearest neighbors can be obtained by sorting according to formulae (5) and (6).…”

Section: Signal Distance Correctormentioning

confidence: 99%

Reliable Wi-Fi Indoor Localization in Case of AP Loss by Using Integrated Model Based on Signal Anomaly Detector and Signal Distance Corrector

Yao

Yang

et al. 2021

Discrete Dynamics in Nature and Society

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When developing a Wi-Fi indoor positioning system in a real-world environment, the problems we have to face are that some access points’ signal strength fluctuates extensively or even loses contact due to the cybersecurity threats, leading to the fact that the indoor location system cannot get reliable application in a real-world environment. To solve this problem, we propose a new integrated model based on signal anomaly detector and signal distance corrector to provide reliable position estimation when the access points’ signal is lost under cybersecurity threats. The signal anomaly detector improves recognition capability of the uncertain signal and noise, while the signal distance corrector improves the robustness and fault tolerance of the highly variable Wi-Fi signals. To fully reflect the performance of the proposed method, experiments have been carried out in the real environment of indoor parking lots. The results show that the proposed integrated model successfully provides reliable position estimation when the access points are lost under cybersecurity threats.

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Section: Signal Distance Correctormentioning

confidence: 99%

Reliable Wi-Fi Indoor Localization in Case of AP Loss by Using Integrated Model Based on Signal Anomaly Detector and Signal Distance Corrector

Yao

Yang

et al. 2021

Discrete Dynamics in Nature and Society

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“…(Parvinnia et al, 2014) also computed a weight for each training object based on a matching strategy. Respectively, (Aguilera et al, 2019) proposed a weighting based on Newton's gravitational force, so that a mass (or relevance) is to be assigned to each instance. Two methods of mass assignment is presented: circled by its own class (CC) and circled by different class (CD).…”

Section: Literature Reviewmentioning

confidence: 99%

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System

Esgin

2020

Proceedings of the 9th International Conference on Operations Research and Enterprise Systems

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In the context of intelligent maintenance, spare part prediction business scenario seeks promising return-oninvestment (ROI) by radically diminishing the hidden costs at after-sales customer services. However, the classification of class-imbalanced data with mixed type features at this business scenario is not straightforward. This paper proposes a hybrid classification model that combines C4.5, Apriori algorithms and weighted k-Nearest Neighbor (kNN) adaptations to overcome potential shortcomings observed at the corresponding business scenario. While proposed approach is implemented within CRISP-DM reference model, the experimental results demonstrate that proposed approach doubles the human-level performance at spare part prediction. This highlights a 50% decrease at the average number of customer visits per fault incident and a significant cutting at the relevant sales and distribution costs. According to best runtime configuration analysis, a real-time spare part prediction model has been deployed at the client's SAP system.

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“…Generally speaking, k-NN only needs one parameter to be adjusted, k, which represents how many closest neighbors are to be considered to classify an unseen object. Once this parameter is set, two main approaches are followed to classify an object, the vote of the majority of the k neighbors, and a weighted vote of all k neighbors considering the distance from where each of them is located concerning the object to classify [11] .…”

Section: K-nearest Neighbor (K-nn)mentioning

confidence: 99%

Classification of Tangerine (Citrus Reticulata Blanco) Quality Using Combination of GLCM, HSV, and K-NN

Listya

Rokhman²

2019

Indonesian J. Comput. Cybern. Syst.

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The quality of fruit production is very important because it is related to the value of sales. Data from the Directorate General of Horticulture at the Ministry of Agriculture in 2017 showed that 94,3% of the total yield of citrus fruits is a type of tangerine. In the classification of the quality, the visual observation process is strongly influenced by subjectivity so that in certain conditions such as tired eyes and the number of oranges that want to classify too many the process can be inconsistent and also take a long time. Therefore, a technology is needed to accelerate the classification process and make it more objective. This study combines the Gray level Co-occurrence Matrix (GLCM) method for texture, Hue, Saturation, Value (HSV) features for color features and the k-Nearest Neighbor (k-NN) classification method. The data used were 60 images of rotten tangerines and 60 images of not rotten tangerines divided using a 4-fold cross-validation method to find the best combination of data training and data testing. 3 main processes will be carried out, namely preprocessing, feature extraction and classification. This study produced the highest accuracy of 80% from the combined of GLCM and HSV features extraction with value k = 5 for k-NN .

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A New Weighted k-Nearest Neighbor Algorithm Based on Newton’s Gravitational Force

Cited by 6 publications

References 14 publications

Reliable Wi-Fi Indoor Localization in Case of AP Loss by Using Integrated Model Based on Signal Anomaly Detector and Signal Distance Corrector

Reliable Wi-Fi Indoor Localization in Case of AP Loss by Using Integrated Model Based on Signal Anomaly Detector and Signal Distance Corrector

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System

Classification of Tangerine (Citrus Reticulata Blanco) Quality Using Combination of GLCM, HSV, and K-NN

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