Using Collaborative Filtering for Dealing with Missing Values in Nuclear Safeguards Evaluation

Nuclear safeguards evaluation aims to verify that countries are not misusing nuclear programs for nuclear weapons purposes. Experts of the International Atomic Energy Agency (IAEA) carry out an evaluation process in which several hundreds of indicators are assessed according to the information obtained from different sources, such as State declarations, on-site inspections, IAEA non-safeguards databases and other open sources. These assessments are synthesized in a hierarchical way to obtain a global assessment. Much information and many sources of information related to nuclear safeguards are vague, imprecise and ill-defined. The use of the fuzzy linguistic approach has provided good results to deal with such uncertainties in this type of problems. However, a new challenge on nuclear safeguards evaluation has attracted the attention of researchers. Due to the complexity and vagueness of the sources of information obtained by IAEA experts and the huge number of indicators involved in the problem, it is common that they cannot assess all of them appearing missing values in the evaluation, which can bias the nuclear safeguards results. This paper proposes a model based on collaborative filtering (CF) techniques to impute missing values and provides a trust measure that indicates the reliability of the nuclear safeguards evaluation with the imputed values.