A classification approach using multi-layered neural networks
Decision Support Systems - Special issue on neural networks for decision support
The nature of statistical learning theory
The nature of statistical learning theory
Neural network design
Modified support vector novelty detector using training data with outliers
Pattern Recognition Letters
On Classifier Domains of Competence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Information Processing and Management: an International Journal
An algorithm to cluster data for efficient classification of support vector machines
Expert Systems with Applications: An International Journal
Modeling consumer situational choice of long distance communication with neural networks
Decision Support Systems
Classification of Unbalanced Medical Data with Weighted Regularized Least Squares
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
Hi-index | 0.00 |
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.