k-Nearest Neighbor algorithm based on feature subspace

Ma, Xiaoyu; Tao, Yang; Chen, Jiangchuan; Liu, Ziyu

doi:10.1109/bdacs53596.2021.00056

Cited by 6 publications

(2 citation statements)

References 2 publications

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“… 11 The Classification Learner results for Q3 (Is there enough signal to distinguish the gut microbiome profiles of responders from non-responders before starting a KD? ), identified the model Ensemble Subspace k-nearest neighbours (kNN) 33 to have the highest estimated accuracy at 71.4%, compared with all available algorithmic models. Ensemble methods combine multiple classifiers, which provides a marked improvement in predictive accuracy and overall performance, particularly when less delineated characteristics exit in the dataset.…”

Section: Resultsmentioning

confidence: 99%

Higher levels of Bifidobacteria and tumor necrosis factor in children with drug-resistant epilepsy are associated with anti-seizure response to the ketogenic diet

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Higher levels of Bifidobacteria and tumor necrosis factor in children with drug-resistant epilepsy are associated with anti-seizure response to the ketogenic diet

et al. 2022

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“…The bagged trees method implements decision trees using bootstrap aggregation (bagging) [35]. The subspace k-NN method uses the random subspace method for k-NN classification [36,37].…”

Section: Signature Classificationmentioning

confidence: 99%

Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures

Guerra-Rosas,

López-Ávila,

Garza-Flores

et al. 2023

Applied Sciences

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This manuscript proposes the possibility of concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eight lesions were analyzed using some algorithms of artificial intelligence: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASCs). Eleven artificial intelligence models were applied so that eight skin lesions could be classified by analyzing the signatures of each lesion. The database was randomly divided into 80% and 20% for the training and test dataset images, respectively. The metrics that are reported are accuracy, sensitivity, specificity, and precision. Each process was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the averages and ± standard deviations were reported for each metric. Although all the results were very satisfactory, the highest average score for the eight lesions analyzed was obtained using the subspace k-NN model, where the test metrics were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision.

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Chloride-Induced Damage Monitoring of Reinforced Concrete Using Ultrasonic Pulse Wave-Based Machine Learning

Mukhti,

Kee

2024

Advanced Structured Materials

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k-Nearest Neighbor algorithm based on feature subspace

Cited by 6 publications

References 2 publications

Higher levels of Bifidobacteria and tumor necrosis factor in children with drug-resistant epilepsy are associated with anti-seizure response to the ketogenic diet

Higher levels of Bifidobacteria and tumor necrosis factor in children with drug-resistant epilepsy are associated with anti-seizure response to the ketogenic diet

Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures

Chloride-Induced Damage Monitoring of Reinforced Concrete Using Ultrasonic Pulse Wave-Based Machine Learning

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