Knowledge acquisition and decision making based on Bayes risk minimization method

There are two central parts in multiple attribute decision making (MADM), which are weight assignment and attribute selection. However, attribute selection is usually ignored in the existing researches, which will result in the difficulty of knowledge acquisition and the error of decision making. In addition, with respect to the data set with labels, the existing methods of weight assignment usually neglect or do not take full advantage of the supervisory function of labels, which may also lead to some decision making mistakes. To make up for these deficiencies, this paper proposes a method for knowledge acquisition and decision making based on Bayes risk minimization. In this method, a novel Bayes risk model based on neighborhood and Gaussian kernel is raised, and a heuristic forward greedy algorithm is designed for attribute selection. Finally, a number of experiments, including the comparison experiments on University of California Irvine (UCI) data and the effectiveness evaluation of fighter, are carried out to illustrate the superiority and applicability of the proposed method.