Intelligent weight generation algorithm based on binary isolation tree

In this paper, in order to make the statistical information of the scoring system more reasonable, a new questionnaire weighting algorithm is proposed to intelligently identify the credibility of survey in this paper. Based on the core idea of the Isolation Forest (iForest), the weights of questionnaire are computationally identified by considering depth of nodes and relative mass between nodes. Using Swee Chuan Tan’s approach of constructing Half-Space Trees, the problem of non-unique weights with repeated calculations is solved and the lower randomness of the result is obtained. In addition, the impact of multiplicity of point on weight is considered by introducing a tiny deviation, and a weight updating method is proposed to deal with the dynamic change of data. The final simulation results demonstrate that the proposed method is defective and accurate, and that the reliability of the questionnaire can be more intelligently recognized in the scoring and evaluation system.