δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions

Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yao’s decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve this problem, a δ-cut decision-theoretic rough set is proposed, which is based on the δ-cut quantitative indiscernibility relation. Furthermore, with respect to criterions of decision-monotonicity and cost decreasing, two different algorithms are designed to compute reducts, respectively. The comparisons between these two algorithms show us the following: (1) with respect to the original data set, the reducts based on decision-monotonicity criterion can generate more rules supported by the lower approximation region and less rules supported by the boundary region, and it follows that the uncertainty which comes from boundary region can be decreased; (2) with respect to the reducts based on decision-monotonicity criterion, the reducts based on cost minimum criterion can obtain the lowest decision costs and the largest approximation qualities. This study suggests potential application areas and new research trends concerning rough set theory

Brain Age Prediction Based on Resting-State Functional MRI Using Similarity Metric Convolutional Neural Network

Brain age prediction is important for understanding brain development and aging. Currently, researchers can predict brain age using resting-state functional MRI (rs-fMRI) data. However, there are differences in brain age development among different subjects, and the same subject also has different development at different ages. So far, how to accurately estimate brain age using rs-fMRI efficiently remains a challenging problem. Therefore, a brain age prediction model with the similarity metric convolutional neural network is proposed in this paper. Specifically, this paper first introduces a siamese convolutional neural network, which includes convolution, batch normalization, and pooling steps simultaneously learns the features of two groups of rs-fMRI, and designs a similarity measurement network. Subsequently, fMRI images of two groups of different subjects are input into the network, and a similarity metirc module is designed to calculate the similarity between the two groups of images. Then the network is optimized by a loss function, and finally, the average value of the three groups of sample labels with the greatest similarity is taken. The absolute mean error and correlation coefficients obtained from the model are 5.337 and 0.6279, respectively. Experimental results show that this method has low mean absolute error and high correlation coefficient on the longitudinal imaging data set of Southwest University