Cross-validation is a widely used model evaluation method in data mining applications. However, it usually takes a lot of effort to determine the appropriate parameter values, such as training data size and the number of experiment runs, to implement a validated evaluation. This study develops an efficient cross-validation method called Complexity-based Efficient (CBE) cross-validation for binary classification problems. CBE cross-validation establishes a complexity index, called the CBE index, by exploring the geometric structure and noise of data. The CBE index is used to calculate the optimal training data size and the number of experiment runs to reduce model evaluation time when dealing with computationally expensive classification data sets. A simulated and three real data sets are employed to validate the performance of the proposed method in the study, while the validation methods compared are repeated random sub-sampling validation and K-fold cross-validation. The results show that CBE cross-validation, repeated random sub-sampling validation and K-fold cross-validation have similar validation performance, except that the training time required for CBE cross-validation is indeed lower than that for the other two methods.
