Basic bounds on cluster error using distortion-rate

Bhatnagar, Jr.

doi:10.1016/j.mlwa.2021.100160

Cited by 1 publication

(2 citation statements)

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“…Many clustering techniques, such as K-means, entail an iterative learning procedure to improve coherence, thereby minimizing the distortion of a cluster [ 16 ]. For the data points inside a cluster, the distortion can be calculated using Sum Square Error (SSE) among each cluster’s points and its centroid [ 17 , 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

Fatlawi

Kiss

2023

Sensors

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Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imbalanced distribution of classes. Many popular oversampling techniques have been designed for imbalanced batch data rather than a continuous stream. This work proposes a self-adjusting window to improve the adaptive classification of an imbalanced data stream based on minimizing cluster distortion. It includes two models; the first chooses only the previous data instances that preserve the coherence of the current chunk’s samples. The second model relaxes the strict filter by excluding the examples of the last chunk. Both models include generating synthetic points for oversampling rather than the actual data points. The evaluation of the proposed models using the Siena EEG dataset showed their ability to improve the performance of several adaptive classifiers. The best results have been obtained using Adaptive Random Forest in which Sensitivity reached 96.83% and Precision reached 99.96%.

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Section: Literature Reviewmentioning

confidence: 99%

“…For the data points inside a cluster, the distortion can be calculated using Sum Square Error (SSE) among each cluster’s points and its centroid [ 17 , 18 ]. SSE, in this case, represents the summation of distances

among data points (

, …,

) and the centroid cn as follows [ 16 ]:

…”

Section: Literature Reviewmentioning

confidence: 99%