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Classification tasks become more challenging when having the curse of dimensionality issue. Recently, there has been an increasing number of datasets with thousands of features. Some classification algorithms often need feature selection to avoid the curse of dimensionality. Genetic programming (GP) has shown success in classification tasks. GP does not require to do feature selection because of its built-in capability to automatically select informative features. However, GP-based methods are often computationally intensive to achieve a good classification accuracy. Based on perspectives from granular computing (GrC), this paper proposes a new approach to linking features hierarchically for GP-based classification. Experiments on seven high-dimensional datasets show the effectiveness of the proposed algorithm in terms of saving training time and enhancing the classification accuracy, compared to baseline methods.

The paper separates decision expression system into three parts: the relation system, the decision-making system and the causal system by the perspective of Pansystems theory. In these three separated systems, the extended approach involves multiple types of attributes and many decision-makers, and it aims at modelling data expressed by monotonic variable consistency measures. Furthermore, the two referred thresholds, according to Bayes decision procedure that is applied by Decision Theoretic Rough Set, can be calculated directly. So the paper proposes Multi-decision-makers-based Monotonic Variable Consistency Rough Set Approach with Multiple Attributes and Criteria, and its properties are proposed and proved.

This paper focuses on the sorting problems with multiple types of attributes. About the attributes, in which are divided into qualitative attributes, quantitative attributes, qualitative criteria and quantitative criteria. Granules of knowledge are defined by applying four types of relations simultaneously: indiscernibility relation defined on qualitative attributes, similarity relation defined on quantitative attributes, dominance relation defined on qualitative criteria and quasi-partial order relation defined on quantitative criteria. To guarantee the tolerance of the system, the threshold is adjusted, resulting in a N-neighborhood system comes into being. The consistency measure which possess properties of monotonicity is regarded as the Likelihood Function, so the optimal threshold is obtained by Maximum Likelihood Estimation, as a result, N-neighborhood system is converted into optimal 1-neighborhood system. Therefore, we proposed the Optimal-Neighborhood Statistics Rough Set Approach with Multiple Attributes and Criteria.