Cautious Classification of Hyper Rectangular, Hyper Circular, and Hyper Oval with a Maximum Symmetric Margin Relative to the Data Edge

A robust classification model is a non-standard model for classifying learning based on an uncertain data set. An incautious model is said to have any meaningless answer to any classification model in its possible set of possible solutions. The optimal answer for a cautious robust classification model for a training data set may not be the hyper-page, in which case it will not be possible to classify the data at the test stage. In this paper, incautious robust classification models are introduced and their problems are investigated and then by changing the loss function of a robust classifier, a cautious robust classification model is presented to prevent incautious. The proposed cautious model is standardized and solutions are provided to reduce the training time and test time. In the experiments, the proposed model was compared with some incautious robust models to classification incomplete training data set, and complete definitive training data set. The results showed that in the incomplete data set, the proposed model had less training time and error rate than incautious models. Also, in the complete definitive data set, the proposed model training time and test time were less than incautious models. The results approved that adding caution to a robust classifier is efficient.