Active Sample Selection Method Based on Decision Making Tendency

A dynamic adjustment and active selection method for rough set decision making based on filtering function is proposed. Firstly, a sample filtering function is defined to determine the basis for sample selection or discarding; then, new samples are added in turn to determine the retention of samples according to the filtering function, and the decision-making tendency of existing samples is adjusted according to the threshold; finally, new sample library is established and attribute reduction is carried out. This method overcomes the problems of complex implementation process and large amount of calculation in traditional variable precision methods, and can effectively remove noise data and improve the robustness of the system. Experimental results show that this method can effectively compress data and improve the quality of sample analysis.