Integrating a three-way decision process with the hasse diagram, optimal scale combination selection

PROJECT TITLE :

Optimal Scale Combination Selection Integrating Three-Way Decision With Hasse Diagram

ABSTRACT:

In the field of Machine Learning, the multi-scale decision system, also known as MDS, is a useful tool for describing hierarchical data. The selection of an optimal scale combination (OSC) and the reduction of attributes are two major challenges that are associated with the process of knowledge discovery in MDSs. Searching for all OSCs, on the other hand, could lead to a combinatorial explosion, and the methods that are currently in use typically involve an excessive amount of time consumption. Within the scope of this investigation, the search for all OSCs is analyzed as an optimization problem, with the scale space functioning as the search space. In light of this, a sequential three-way decision model of the scale space has been developed with the goal of decreasing the size of the search space. This is accomplished by combining the three-way decision with the Hasse diagram. First, a novel scale combination is proposed to perform scale selection and attribute reduction simultaneously. Next, an extended stepwise optimal scale selection (ESOSS) method is introduced to rapidly search for a single local OSC on a subset of the scale space. Finally, a novel scale combination is proposed to perform scale selection and attribute reduction simultaneously. Second, based on the obtained local OSCs, a sequential three-way decision model of the scale space is established. This model is used to divide the search space into three pair-wise disjoint regions, which are referred to as the positive, negative, and boundary regions, respectively. It is possible to demonstrate that a local OSC on the boundary region is also a global OSC, and this leads to the conclusion that the boundary region should be regarded as a new search space. Therefore, it is possible to obtain all of the OSCs of a particular MDS by performing a step-by-step search for the OSCs that are local to the boundary regions of the MDS. In conclusion, according to the characteristics of the Hasse diagram, a formula is provided for calculating the maximal elements of a given boundary region in order to reduce the amount of space complexity. This is done in order to simplify the process. As a result, an effective OSC selection algorithm has been proposed in order to improve the efficiency of searching for all OSCs by reducing the search space. The findings of the experiments show that the proposed method is capable of significantly reducing the amount of time spent computing.

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