Abstract
Chapter 1 stressed the significance of interpretability for the purpose of knowledge discovery. This chapter introduces theoretical aspects of interpretability on rule based systems. In particular, some impact factors are identified and how these factors have an impact on interpretability is also analyzed. In addition, some criteria for evaluation on interpretability are also listed.
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References
Stahl, F., Jordanov, I.: An overview on the use of neural networks for data mining. WIREs Data Min. Knowl. Disc. 3(2), 193–208 (2012)
Furnkranz, J.: Separate-and-conquer rule learning. Artif. Intell. Rev. 13, 3–54 (1999)
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© 2016 Springer International Publishing Switzerland
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Liu, H., Gegov, A., Cocea, M. (2016). Interpretability Analysis. In: Rule Based Systems for Big Data. Studies in Big Data, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-23696-4_7
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DOI: https://doi.org/10.1007/978-3-319-23696-4_7
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