A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

Saadatfar, Hamid; Khosravi, Samiyeh; Joloudari, Javad Hassannataj; Mosavi, Amir; Shamshirband, Shahaboddin

doi:10.3390/math8020286

Cited by 84 publications

(40 citation statements)

References 23 publications

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“…However, if K = 5, two points in the neighborhood are in Class A, and three are in Class B, so the new data point will be classified as Class B. It follows that the choice of the value of K has a big impact on the accuracy of the trained model [58]. There is no specific way to determine the best K value, so it is necessary to try different values to find the best one.…”

Section: K-nearest Neighborsmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

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Section: K-nearest Neighborsmentioning

confidence: 99%

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Pietrzak

Wolkiewicz

2021

Electronics

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“…There are many methods of analysis in the field of using the electronic nose in beekeeping, including linear discriminant analysis (LDA), principal component analysis (PCA), and cluster analysis (CA) with the furthest neighbor method (kNN). Good results have also been obtained using the artificial neural network (ANN) machine learning techniques, which use a neural network model based on a multilayer perceptron that learned using a backpropagation algorithm [8][9][10][11][12][13][14][15].…”

Section: Achievements To Date In the Use Of Gas Sensors For This Typementioning

confidence: 99%

Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Wilk

Bąk

Artiemjew

et al. 2020

Sensors

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Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

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“…It is based on the Bayesian system [24] and is used when the number of inputs is too big. It is mostly used in mathematics and statistical fields.…”

Section: A Native Bayes Algorithmmentioning

confidence: 99%

Machine Learning-based Web Application for Early Diagnosis of Diabetes

Shahwani

2020

Journal of Applied and Emerging Sciences

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Diabetes has become a chronic disease that seriously threatens human health. It is a group of metabolic diseases characterized by hyperglycemia and there is no role of the age factor involved. The long-term of diabetes disease causes chronic damage and dysfunction of various tissues, especially the eyes, kidneys, heart, blood vessels, and nerves. Most of the time people are not sure about this common disease at the early stage and unluckily the patient moves to a critical situation to meet with major disease due to the continuous effect of diabetes. This research is conducted to build the machine learning-based web application platform for the early diagnosis of the disease, freely accessible anywhere anytime. We used the benchmark dataset named PIDD (Prima Indian Diabetes Dataset) and performed the comparative analysis among the Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest and Support Vector Machines. Based on the classification performance, we found that SVM performed the best among the pool of mentioned algorithms and, therefore, adopted for the development of the intelligent web application for the diabetes diagnosis.

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A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning

Cited by 84 publications

References 23 publications

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Machine Learning-based Web Application for Early Diagnosis of Diabetes

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