Enhanced Cross-Validation Methods Leveraging Clustering Techniques

The efficacy of emerging and established learning algorithms warrants scrutiny. This examination is intrinsically linked to the results of classification performance. The primary determinant influencing these results is the distribution of the training and test data presented to the algorithms. Existing literature frequently employs standard and stratified (S-CV and St-CV) k-fold cross-validation methods for the creation of training and test data for classification tasks. In the S-CV method, training and test groups are formed via random data distribution, potentially undermining the reliability of performance results calculated post-classification. This study introduces innovative cross-validation strategies based on k -means and k-medoids clustering to address this challenge. These strategies are designed to tackle issues emerging from random data distribution. The proposed methods autonomously determine the number of clusters and folds. Initially, the number of clusters is established via Silhouette analysis, followed by identifying the number of folds according to the data volume within these clusters. An additional aim of this study is to minimize the standard deviation (Std) values between the folds. Particularly in classifying large datasets, the minimized Std negates the need to present each fold to the system, thereby reducing time expenditure and system congestion/fatigue. Analyses were carried out on several large-scale datasets to demonstrate the superiority of these new CV methods over the S-CV and St-CV techniques. The findings revealed superior performance results for the novel strategies. For instance, while the minimum Std value between folds was 0.022, the maximum accuracy rate achieved was approximately 100%. Owing to the proposed methods, the discrepancy between the performance outputs of each fold and the overall average is statistically minimized. The randomness in creating the training/test groups, which has been previously identified as a negative contributing factor to this discrepancy, has been significantly reduced. Hence, this study is anticipated to fill a critical and substantial gap in the existing literature concerning the formation of training/test groups in various classification problems and the statistical accuracy of performance results.