Abstract
A new form of conditional rules is proposed for regression problems in which a rule associates an input label with a corresponding image label on the output space. Here input labels are interpreted in terms of random set and prototype theory, so that each label is defined by a random set neighbourhood around a prototypical value. Within this framework we propose a rule learning algorithm and test its effectiveness on a number of benchmark regression data sets. Accuracy is compared with other several state-of-the-art regression algorithms, suggesting that our approach has the potential to be an effective rule learning methodology.
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Li, G., Lawry, J. (2013). A Random Set and Rule-Based Regression Model Incorporating Image Labels. In: Qin, Z., Huynh, VN. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2013. Lecture Notes in Computer Science(), vol 8032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39515-4_9
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DOI: https://doi.org/10.1007/978-3-642-39515-4_9
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