A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean

Kumbure, Mahinda Mailagaha; Luukka, Pasi; Collan, Mikael

doi:10.1016/j.patrec.2020.10.005

Cited by 64 publications

(32 citation statements)

References 33 publications

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“…which is the number of nearest neighbors, for calculation of the fuzzy membership degree, and also and which are parameters for the Bonferroni mean. For the value of in assigning the fuzzy membership degree, we used = 2 as recommended in [17]. We searched for the optimal value of each dataset by ranging the value of between [2,10].…”

Section: Preprocessingmentioning

confidence: 99%

“…To solve this, Keller et al [15] proposed the FKNN and introduced the theory of fuzzy sets into the KNN algorithm and developed a fuzzy version of KNN. The FKNN technique executes the class label assignment process by giving the unclassified sample a degree of membership in each class [17]. This was further developed by Sarkar [18] in which the class assignment process was correlated with the uncertainty caused by overlapping classes and insufficient features.…”

Section: Introductionmentioning

confidence: 99%

“…The class label of the closest pseudo nearest neighbor to the unclassified sample is assigned to the unclassified sample. Kumbure et al [17] used the Bonferroni mean aggregator to calculate the local mean vector due to it having parameters that can be changed depending on the presented problem to develop the BM-FKNN.…”

Section: Introductionmentioning

confidence: 99%

“…The use of local mean vectors can help to overcome this problem. Assigning the class of an unclassified sample based on the closest local mean vector in favor of the original majority vote principle, will lessen the domination of the majority class over the minority class [17].…”

Section: Introductionmentioning

confidence: 99%

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A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier

Widyadhana

Putra

Indraswari

et al. 2021

Jurnal Ilmu Komputer dan Informasi

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K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that assigns class label of a query sample dependent on the nearest local centroid mean vector to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the Nearest Centroid Neighborhood (NCN) concept also considers spatial distribution and symmetrical placement of the neighbors. Also, the proposed classifier can overcome class domination of its neighbors in datasets with class imbalance because it averages all the centroid vectors from each class to adequately interpret the distribution of the classes. The BM-FKNCN classifier is tested on datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and benchmarked with classification results from the KNN, Fuzzy-KNN (FKNN), BM-FKNN and FKNCN classifiers. The experimental results show that the BM-FKNCN achieves the highest overall average classification accuracy of 89.86% compared to the other four classifiers.

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Section: Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier

Widyadhana

Putra

Indraswari

et al. 2021

Jurnal Ilmu Komputer dan Informasi

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“…Despite being a method that has been used for several years, many variations of this algorithm are currently being evaluated to improve its capacity. The papers from Mailagaha Kumbure et al [24] and González et al [25] combine the k-NN algorithm with systems based on fuzzy logic to improve its accuracy. As presented by Sharma and Seal [26], the way of calculating the distances of this algorithm is an important parameter to be evaluated, for this reason the distance calculation method will be a parameter evaluated in this paper.…”

Section: Introductionmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Corso¹,

Perez²,

Stefenon³

et al. 2021

Preprint

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The contamination on the insulators may increase its surface conductivity and, as a consequence, electrical discharges occur more frequently, which can lead to interruptions in the power supply. To maintain reliability in the electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance, is a difficult task as there are several levels of contamination that are hardly noticed during inspections. To improve the quality of inspections, this paper proposes to use the k-nearest neighbours (k-NN) to classify the levels of insulator contamination, based on the image of insulators at various levels of contamination simulated in the laboratory. Using computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed results of up to 85.17 % accuracy using the k-fold cross-validation method, with an average accuracy higher than 82 % for multi-classification of the contamination of the insulators, being superior to the compared models.

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