Abstract
Interpretation and visualization of the classification models are important parts of machine learning. Rule-based classifiers often contain too many rules to be easily interpreted by humans, and methods for post-classification analysis of the rules are needed. Here we present a strategy for circular visualization of sets of classification rules. The Circos software was used to generate graphs showing all pairs of conditions that were present in the rules as edges inside a circle. We showed using simulated data that all two-way interactions in the data were found by the classifier and displayed in the graph, although the single attributes were constructed to have no correlation to the decision class. For all examples we used rules trained using the rough set theory, but the visualization would by applicable to any sort of classification rules. This method for rule visualization may be useful for applications where interaction terms are expected, and the size of the model limits the interpretability.
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Bornelöv, S., Enroth, S., Komorowski, J. (2012). Visualization of Rules in Rule-Based Classifiers. In: Watada, J., Watanabe, T., Phillips-Wren, G., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29977-3_33
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DOI: https://doi.org/10.1007/978-3-642-29977-3_33
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