A novel adaptive k-NN classifier for handling imbalance: Application to brain MRI

Kirtania, Ritaban; Shankar, B. Uma

doi:10.3233/ida-194647

Cited by 5 publications

(3 citation statements)

References 61 publications

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“…Several papers on the KNN technique and its development included Alsammak et al (2020) conducting research to improve the performance of the K-Nearest Neighbor (KNN) classifier to satisfy emerging big data requirements. Kirtania et al (2020) Wilson & Martinez (1997a) explained that one of the distances for numerical and categorical data types is the Heterogeneous Euclidean-Overlap Metric (HEOM). HEOM handles both continuous and nominal attributes with overlap metric for nominal attributes and normalize Euclidean distance for linear attributes (ChitraDevi et al, 2012;Tusyakdiah, 2021).…”

Section: Datamentioning

confidence: 99%

Implementation of K-Nearest Neighbor using the oversampling technique on mixed data for the classification of household welfare status

Djafar,

Fauzan

2024

SiTns

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Welfare is closely related to poverty and the socio-economic disparities in a society. Based on data from the Central Bureau of Statistics, Kulon Progo in Indonesia had the highest poverty rate in the province of the Special Region of Yogyakarta; an increasing trend was observed every year from 2019 to 2021; Kulon Progo also had a low poverty line (after Gunung Kidul) compared to other regencies/cities in this province. This study aimed to classify the household welfare status in Kulon Progo in March 2021 using the K-Nearest Neighbor (KNN) method. Since imbalance was found between the poor and non-poor classes, an oversampling technique was employed. Imbalanced data affect classification, particularly when predicting the results of the classification. The following oversampling techniques were employed in this study: Random Oversampling (RO), the Adaptive Synthetic (ADASYN) and the Synthetic Minority Oversampling Technique (SMOTE). It was found that, of the three techniques, RO was the most efficient with k = 5, which yielded the best performance in terms of sensitivity, specificity, the G-mean, and accuracy reaching 0.643, 0.805, 0.719, and 78.873%, respectively. Therefore, it can be concluded that the classification model performed well enough to classify household welfare status, especially among the poor (minority class).

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Section: Datamentioning

confidence: 99%

Implementation of K-Nearest Neighbor using the oversampling technique on mixed data for the classification of household welfare status

Djafar,

Fauzan

2024

SiTns

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Section: Datamentioning

confidence: 99%

Untitled

2024

SiTns

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Statistics in Transition new series (SiTns) is an international journal published jointly by the Polish Statistical Association (PTS) and Statistics Poland, on a quarterly basis (during 1993-2006 it was issued twice and since 2006 three times a year). Also, it has extended its scope of interest beyond its originally primary focus on statistical issues pertinent to transition from centrally planned to a market-oriented economy through embracing questions related to systemic transformations of and within the national statistical systems, world-wide.The SiTns seeks contributors that address the full range of problems involved in data production, data dissemination and utilization, providing international community of statisticians and users -including researchers, teachers, policy makers and the general public -with a platform for exchange of ideas and for sharing best practices in all areas of the development of statistics.Accordingly, articles dealing with any topics of statistics and its advancement -as either a scientific domain (new research and data analysis methods) or as a domain of informational infrastructure of the economy, society and the state -are appropriate for Statistics in Transition new series.Demonstration of the role played by statistical research and data in economic growth and social progress (both locally and globally), including better-informed decisions and greater participation of citizens, are of particular interest.Each paper submitted by prospective authors are peer reviewed by internationally recognized experts, who are guided in their decisions about the publication by criteria of originality and overall quality, including its content and form, and of potential interest to readers (esp. professionals).

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“…Traditional machine learning methods such as K-Nearest Neighbors (KNN) [5], Decision Trees (DT) [6], Support Vector Machines (SVM), and Logistic Regression (LR) have achieved some remarkable results in this field [7].…”

Section: Introductionmentioning

confidence: 99%

Prediction for diabetes by a weighted voting classifier based on normalized AUC with balancing factors

Gao

Zheng

2023

Preprint

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Diabetes is a metabolic disease characterized by hyperglycemia caused by insulin deficiency or resistance. Diabetes can lead to various co-morbidities and is a common medical problem worldwide. The prevention and control of diabetes would benefit from accurately identifying diabetic individuals. In this study, we test the best pipeline of different machine learning (ML) models (K-Nearest Neighbor, Random Forest, Support Vector Machine, XGBoost) under different pre-processing. Also, this study investigates and proposes a weighted soft-voting classification model (NAWVE), which uses the AUC obtained from the base model on the training set and adds a balancing factor as a weight after normalization. This integrated model can well balance different performance classifiers. In the prediction of diabetes, NAWVE received the highest scores in four metrics, with accuracy, AUC, f1, and recall of 0.9606, 0.9621, 0.9472, and 0.9677, respectively. Our proposed fusion model outperformed any individual classification model and Stacking with meta-learner as logistic regression.

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A novel adaptive k-NN classifier for handling imbalance: Application to brain MRI

Cited by 5 publications

References 61 publications

Implementation of K-Nearest Neighbor using the oversampling technique on mixed data for the classification of household welfare status

Implementation of K-Nearest Neighbor using the oversampling technique on mixed data for the classification of household welfare status

Untitled

Prediction for diabetes by a weighted voting classifier based on normalized AUC with balancing factors

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