Abstract
This work uses the patterns learnt by machine learning models to convert the hard labels into probabilistic labels. The probabilistic labels by different learning models are combined using multi-criteria decision-making approaches. Preference order of alternatives is generated based on the decision probabilities assigned by these approaches. Both the preference order of alternatives and the patterns of learning models are provided as explainable knowledge, which gives a better interpretation about the decision space to a decision-maker. The choice of the final decision alternative depends on the decision-maker. The efficiency and the robustness of proposed decision-making model is verified over publicly available datasets. It can be observed from results that the performance of TOPSIS and PROMETHEE are equal for 9 out of 12 datasets.
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Kavya, R., Gupta, S., Christopher, J., Panda, S. (2023). Explainable Decision Making Model by Interpreting Classification Algorithms. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-031-35507-3_31
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DOI: https://doi.org/10.1007/978-3-031-35507-3_31
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