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Research aims to improve the efficiency of the algorithm by not compromising the accuracy. Now we discuss the methodology to update approximations of DRSA when new objects enter the system at time t+1. In first step, we update decision classes to include new objects. Pseudocode is given here. As an input, we have old dataset, previous information of decision classes, set of new objects, total number of decision classes (with an assumption that any change in object set does not affect the decision classes). This step generates updated decision classes and respective higher and lower preference ordered classes.
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IEEE/ICACT20220156 Slide.17
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