Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation

By Tzu-Tsung Wong. Pattern Recognition, 2015

1,423 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/4732a77c3753c0a5f31c62ce2e38407d6a945ebf

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Citations in the corpus

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, methodology): “We used a k-fold cross-validation method to test the network performance [24].”
Drought forecasting through statistical models using standardised precipitation index: a systematic review and meta-regression analysis (2019, result): “2017b; Wong 2015), and this method was also noted in some studies.”
Reliable Accuracy Estimates from k-Fold Cross Validation (2020, background): “The six books summarized in [8] do not have consistent conclusion about whether k - fold cross validation should be repeatedly performed.”
State-of-the-art review of soft computing applications in underground excavations (2020, methodology): “In addition, to avoid bias in data selection, one of the most popular validation methods, 5-fold cross-validation (Kohavi, 1995; Wong, 2015), was employed in this study during the process of data pattern determination and the comprehensive model assessment.”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, background): “, 5), and AUC is calculated on the ith test set [93].”
Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, methodology): “Three popular resampling methods are random splitting [33], cross-validation [34], and bootstrapping.”
Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification (2016, background): “The above mentioned author wrote an article “Performance evaluation of classification algorithms by k-fold and leaveone-out cross-validation” [17].”
Abnormal Brain Connectivity Patterns in Children with Global Developmental Delay Accompanied by Cognitive Impairment: A Resting-State EEG Study. (2025, unknown): “A 10-fold cross-validation strategy was adopted (90% data for training and 10% for testing per fold), with model performance evaluated across five dimensions: Accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AUC) [ 30].”
Unveiling differential adverse event profiles in vaccines via LLM text embeddings and ontology semantic analysis (2025, unknown): “To comprehensively assess the optimized models’ robustness, a final 10-fold cross-validation evaluation [27] was conducted on the entire dataset.”