A processing device and method of classifying data are provided. The method comprises the computer-implemented steps of selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets, generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers, testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier, and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of classifying data, comprising the computer-implemented steps of: selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets; generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers; testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier; and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers. 2. The method of claim 1, further comprising normalizing the weight values of the 3D array of classifiers. 3. The method of claim 1, wherein when operated on an inputted data set, the 3D array of classifiers returns a prediction. 4. The method of claim 1, wherein when operated on an inputted data set, the 3D array of classifiers returns a prediction as a probability distribution over categories. 5. The method of claim 1, wherein the 3D array of classifiers comprises a comparison of model sets in the M number of model sets. 6. The method of claim 1, wherein the 3D array of classifiers comprises a comparison of data representation sets in the R number of data representation sets. 7. The method of claim 1, wherein the 3D array of classifiers comprises a comparison of sampling sets in the T number of sampling sets. 8. The method of claim 1, with the generating the M*R*T number of classifiers comprising training and testing the M*R*T number of classifiers. 9. The method of claim 1, with the generating the M*R*T number of classifiers comprising training and testing the M*R*T number of classifiers, wherein the training and the testing are performed substantially in parallel. 10. The method of claim 1, wherein the models comprise one or more of a parameterized support vector machine, a logistic regression model, a decision tree, or a neural network. 11. The method of claim 1, wherein a particular accuracy score of a particular individual classifier is converted into a particular weight value for the particular individual classifier using a predetermined statistical analysis. 12. The method of claim 1, wherein the selecting and generating are performed using large-scale parallel computing. 13. The method of claim 1, further comprising employing the 3D array of classifiers for data representation optimization, including feature selection. 14. The method of claim 1, wherein the 3D array of classifiers reveals latent relationships between data representation selection and model selection. 15. A processing device, comprising: a non-transitory memory storage comprising instructions; and one or more processors in communication with the memory, wherein the one or more processors execute the instructions to: select a M number of model sets, a R number of data representation sets, and a T number of sampling sets; generate a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers; test each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier; and assign a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers. 16. The processing device of claim 15, further comprising normalizing the weight values of the 3D array of classifiers. 17. The processing device of claim 15, wherein when operated on an inputted data set, the 3D array of classifiers returns a prediction. 18. The processing device of claim 15, wherein when operated on an inputted data set, the 3D array of classifiers returns a prediction as a probability distribution over categories. 19. The processing device of claim 15, wherein the 3D array of classifiers comprises a comparison of model sets in the M number of model sets. 20. The processing device of claim 15, wherein the 3D array of classifiers comprises a comparison of data representation sets in the R number of data representation sets. 21. The processing device of claim 15, wherein the 3D array of classifiers comprises a comparison of sampling sets in the T number of sampling sets. 22. The processing device of claim 15, with the generating the M*R*T number of classifiers comprising training and testing the M*R*T number of classifiers. 23. The processing device of claim 15, with the generating the M*R*T number of classifiers comprising training and testing the M*R*T number of classifiers, wherein the training and the testing are performed substantially in parallel. 24. The processing device of claim 15, wherein the models comprise one or more of a parameterized support vector machine, a logistic regression model, a decision tree, or a neural network. 25. The processing device of claim 15, wherein a particular accuracy score of a particular individual classifier is converted into a particular weight value for the particular individual classifier using a predetermined statistical analysis. 26. The processing device of claim 15, wherein the selecting and generating are performed using large-scale parallel computing. 27. The processing device of claim 15, further comprising employing the 3D array of classifiers for data representation optimization, including feature selection. 28. The processing device of claim 15, wherein the 3D array of classifiers reveals latent relationships between data representation selection and model selection.