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I am developing algorithm for solving following problem:

Given a set of items with unknown feature(s) (but know distribution of feature(s)). Algorithm must choose what items to measure(every measure has some cost)). I use Value of information theory to find this measurements.

After measurements are done algorithm choose K best items from the set, using some utility function that depends on feature values of items.

I crafted few synthetic data sets, but perhaps there are some benchmark data sets that are used for this kind of problems?

Regards.

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In this sort of situation, two standard answers are:

  1. Figure out what the practical applications of your algorithm are. Find a dataset associated with that particular application, and try your algorithm on it and see how well it works. Measure success using some metric that is appropriate for that particular application.

  2. Look through the research literature to find previously published papers that try to solve the same problem. Look at what benchmarks they used. Use the same benchmarks, so that you can compare how well your algorithm does to previously published algorithms.

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