The user determines the explainability of hypotheses in intelligent decision support systems in terms of their reasoning. The paper discusses the importance of reasoning and different approaches to reasoning in intelligent systems. Given a set of hypotheses, there is a decomposition of inference rules for similar difficult-to-recognize diseases or pathological conditions. This may be due to partially overlapping features (risk factors). Simultaneously, there arises the problem of forming a ranked list of hypotheses accompanied by all arguments, including arguments of the lowest level, i.e., those related to weak hypotheses. This paper considers a modification of the reasoning algorithm for argumentation reasoning. This provides a gentle reduction in the number of hypotheses by selecting one or more leading ones corresponding to the presence of more than one subclass or group of diseases. In addition, the authors use the method of assigning an order relation. The authors present and justify the reasoning algorithm modification within the framework of the previously created knowledge base of the intelligent recommendation system for disease risk assessment. The system is implemented on a heterogeneous semantic network. The algorithm steps are corrected. The solver ranks issued hypotheses while storing information about all detected arguments regardless of their relevance. The modified solver includes prevention against possible loss of one of the relevant hypotheses when there is a number of diseases present at the same time. It provides information about all reasons for multiple hypotheses of different ranks. The solver enhances the explainability of the issued hypotheses based on features that give reasons for different classified diseases. The authors compare the modified algorithm with other approaches that interpret the issued solutions. The practical significance of the work is in increasing the explainability for the user of the leading hypotheses while simultaneously retrieving the entire set of detected arguments.
DOI: 10.15827/0236-235X.150.297-304
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Nikolaev, A. A., Blagosklonov, N. A., Kobrinsky, B. A. Modifying the reasoning algorithm in classification tasks // Software & Systems, 2025, 38(2), pp. 297–304.