A density weighted fuzzy outlier clustering approach for class imbalanced learning

Wang, Xiaokang; Wang, Huiwen; Wang, Yihui

doi:10.1007/s00521-020-04747-4

Cited by 12 publications

(1 citation statement)

References 31 publications

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“…But the disadvantage is that it cannot perform well 1,2 The authors are with Faculty of Informatics Burapha University, Thailand., E-mail: 61910138@go.buu.ac.th and rasmequa@go.buu.ac.th 2 Corresponding author: rasmequa@go.buu.ac.th This work is funded by the faculty of Informatics, Burapha University, scal year 2019. Xiaokang et al [4], proposed a density-weighted fuzzy outlier clustering approach for class imbalanced learning as a method for clustering fuzzy outlier clusters. This method considers the relationship of new ambiguous neighborhoods with local density data.…”

Section: Introductionmentioning

confidence: 99%

Outlier Detection in Wellness Data using Probabilistic Mapped Mean-Shift Algorithms

Phongsasiri¹,

Rasmequan²

2021

ECTI-CIT

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In this paper, the Probabilistic Mapped Mean-Shift Algorithm is proposed to detect anomalous data in public datasets and local hospital children’s wellness clinic databases. The proposed framework consists of two main parts. First, the Probabilistic Mapping step consists of k-NN instance acquisition, data distribution calculation, and data point reposition. Truncated Gaussian Distribution (TGD) was used for controlling the boundary of the mapped points. Second, the Outlier Detection step consists of outlier score calculation and outlier selection. Experimental results show that the proposed algorithm outperformed the existing algorithms with real-world benchmark datasets and a Children’s Wellness Clinic dataset (CWD). Outlier detection accuracy obtained from the proposed algorithm based on Wellness, Stamps, Arrhythmia, Pima, and Parkinson datasets was 93%, 94%, 80%, 75%, and 72%, respectively.

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Section: Introductionmentioning

confidence: 99%