Abstract
When facing a data mining task, human experts tend to be responsible for proposing the hypotheses that lead to the discovery of interesting patterns. Recently, there is interest in automating the hypothesis generation process to reduce the load on the human expert during data mining. However, if we want an artificial agent to undertake this new role, we also need new metrics to measure the success of the hypothesis generation mechanism. This paper explores the design of metrics for evaluating hypothesis generation algorithms in terms of differences in the way they focus attention in the data mining search-space. We demonstrate our new metrics applied to three stochastic search based prototype hypothesis generation algorithms. Results show that some differences in attention focus can be identified using our metrics. Directions for further work in attention focus metrics and hypothesis generation algorithms are discussed.
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© 2012 Springer-Verlag Berlin Heidelberg
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Wang, B., Merrick, K.E., Abbass, H.A. (2012). Developing Attention Focus Metrics for Autonomous Hypothesis Generation in Data Mining. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_29
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DOI: https://doi.org/10.1007/978-3-642-34859-4_29
Publisher Name: Springer, Berlin, Heidelberg
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