| Autor: |
Bar, Abhimanyu, Kumar, Anil, Sai Prasad, P. S. V. S. |
| Zdroj: |
Granular Computing; January 2023, Vol. 8 Issue: 1 p45-66, 22p |
| Abstrakt: |
The optimal reduct computation problem aims to obtain the best reduct out of all possible reducts of a given decision system. In the rough set literature, two optimality criteria exist for computing an optimal reduct: shortest length based and coarsest granular space based. The coarsest granular space-based optimal reduct has the ability to induce a better generalizable classification model. The A∗RSORis an existing A∗search-based optimal reduct computation algorithm that uses the coarsest granular space as an optimality criterion. This article proposes an improved coarsest granularity-based optimal reduct approach MA∗_RSORthrough analyzing the search process’s behaviour in A∗RSORalgorithm. To minimize the search space utilization and arrive at an optimal reduct in less time, suitable modifications are incorporated using the domain knowledge of rough set theory. The relevance of MA∗_RSORis demonstrated through theoretical analysis and comparative experimental validation with state-of-the-art algorithms. The experimental results with benchmark data sets established that MA∗_RSORachieves significant computational time gain (49-99%) and space reduction (37-96%) over A∗RSOR. The MA∗_RSORcould induce classification models with significantly better classification accuracies than state-of-the-art shortest length-based optimal/near-optimal reduct computation algorithms. In addition, a coefficient of variation based CVNonCoreheuristic is proposed for predicting when the MA∗_RSORalgorithm is appropriate to use. Experimental results validate the relevance of the heuristic as its prediction turned out correctly in 8 out of 10 data sets. |
| Databáze: |
Supplemental Index |
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