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objects which we can be certain belong to target set Xdisplaystyle X, but there are objects which we can definitively exclude from set Xdisplaystyle. International Journal of General Systems. The reduct of an information system is not unique : there may be many subsets of attributes which preserve the equivalence-class structure (i.e., the knowledge) expressed in the information system. There are a few rule-extraction methods. In the example information system above, another reduct is P1,P2,P5displaystyle P_1,P_2,P_5, producing the same equivalence-class structure as xPdisplaystyle x_P. We design, implement and evaluate a series of pattern classifiers with various parameters in order to compare their performances in a real data set from the SIS system.
Using Variable Precision Rough Set for Selection and Classification into the classication process by means of Variable Precision Rough Set Theory (vprs). Each attribute set induces an (indiscernibility) equivalence class structure, the equivalence classes induced by Pdisplaystyle P given by xPdisplaystyle x_P, and the equivalence classes induced by Qdisplaystyle Q given by xQdisplaystyle x_Q. The pair (U, R) is called the approximation space. Attribute dependency edit One of the most important aspects of database analysis or data acquisition is the discovery of attribute dependencies; that is, we wish to discover which variables are strongly related to which other variables. These upper and lower thresholds determine region inclusion for elements. The LEM2 algorithm of lers is frequently used for rule induction and is used not only in lers but also in other systems,.g., in rses (Bazan. Unlike other methods, as those given above, classical rough set analysis requires no additional information, external parameters, models, functions, grades or subjective interpretations to determine set membership instead it critical thinking attitudes only uses the information presented within the given data (Düntsch and Gediga 1995). Clearly, when the upper and lower approximations are equal (i.e., boundary region empty then P(X)1displaystyle alpha _P(X)1, and the approximation is perfect; at the other extreme, whenever the lower approximation is empty, the accuracy is zero (regardless of the size of the upper approximation). (Note that objects with P41displaystyle P_4neq 1 in this case are simply the objects that have P42displaystyle P_42, but in general, P41displaystyle P_4neq 1 would include all objects having any value for P4displaystyle P_4 other than P41displaystyle P_41, and there may be several such classes.
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