Granular Computing in Decision Approximation

Abstract

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Boosting Effect of Classifier Based on Simple Granules of Knowledge*

“…In both cases, the accuracy can be lowered. The best way is to use the optimal radii of classification with the proper size of granules [14]. In conclusion, the CSG can be effectively boosted what was shown in experiments presented in this work.…”

“…Somebody can ask about the optimality of parameters for CSG classifier and the way of proper parameters selection. The optimal parameters for CSG classifier can be obtained by double granulation of data sets, without any classification process, which was shown in [14]. Exemplary result for Bagging based on Arcing, Australian Credit dataset, Mean size of classification granules is 8.4, the radius = 0.571429…”

“…In the recent works, we have developed a classifier based on simple granules of knowledge (CSG) [1,2,14]. This method is in the group of rough set classifiers based on the granular structures developed by Polkowski [15].…”

On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation

“…For homogeneous granulation, please refer to Tables 7-11. As a conclusion of the research presented in [22], we can say that granulation is an effective technique of absorbing some degree of missing values placed in the dataset. Our observations were proved by comparable classification results with the non-missing values data case.…”

“…This is a work about using granular rough computing techniques to absorb missing values [19]. The exact theoretical introduction to the family of approximation methods to which our methods belong to can be found in [20][21][22]. Of course, to understand the body of the algorithmic work, we have included all the relevant details in the following sections.…”