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Keywords = Location-based Nearest Neighbor (LBNN)

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17 pages, 541 KB  
Article
Utilizing Nearest-Neighbor Clustering for Addressing Imbalanced Datasets in Bioengineering
by Chih-Ming Huang, Chun-Hung Lin, Chuan-Sheng Hung, Wun-Hui Zeng, You-Cheng Zheng and Chih-Min Tsai
Bioengineering 2024, 11(4), 345; https://doi.org/10.3390/bioengineering11040345 - 31 Mar 2024
Cited by 1 | Viewed by 1509
Abstract
Imbalance classification is common in scenarios like fault diagnosis, intrusion detection, and medical diagnosis, where obtaining abnormal data is difficult. This article addresses a one-class problem, implementing and refining the One-Class Nearest-Neighbor (OCNN) algorithm. The original inter-quartile range mechanism is replaced with the [...] Read more.
Imbalance classification is common in scenarios like fault diagnosis, intrusion detection, and medical diagnosis, where obtaining abnormal data is difficult. This article addresses a one-class problem, implementing and refining the One-Class Nearest-Neighbor (OCNN) algorithm. The original inter-quartile range mechanism is replaced with the K-means with outlier removal (KMOR) algorithm for efficient outlier identification in the target class. Parameters are optimized by treating these outliers as non-target-class samples. A new algorithm, the Location-based Nearest-Neighbor (LBNN) algorithm, clusters one-class training data using KMOR and calculates the farthest distance and percentile for each test data point to determine if it belongs to the target class. Experiments cover parameter studies, validation on eight standard imbalanced datasets from KEEL, and three applications on real medical imbalanced datasets. Results show superior performance in precision, recall, and G-means compared to traditional classification models, making it effective for handling imbalanced data challenges. Full article
(This article belongs to the Special Issue Computer Vision and Machine Learning in Medical Applications)
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