Conditional Preference Networks (CP-nets) have been proposed for modeling and reasoning about combinatorial decision domains. However, the study of CP-nets learning has not advanced sufficiently for their widespread use in complex, real-world applications where the problem is large-scale and the data is not clean. In many real world applications, due to either the randomness of the users’ behaviors or the observation errors, the data-set in hand could be inconsistent, i.e., there exists at least one outcome preferred over itself in the data-set. In this work, we present an evolutionary-based method for solving the CP-net learning problem from inconsistent examples. Here, we do not learn the CP-nets directly. Instead, we frame the problem of learning into an optimization problem and use the power of evolutionary algorithms to find the optimal CP-net. The experiments indicate that the proposed approach is able to find a good quality CP-net and outperforms the current state-of-the-art algorithms in terms of both sample agreement and graph similarity.
CITATION STYLE
Haqqani, M., & Li, X. (2017). An evolutionary approach for learning conditional preference networks from inconsistent examples. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10604 LNAI, pp. 502–515). Springer Verlag. https://doi.org/10.1007/978-3-319-69179-4_35
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