Abstract
No matter how great a tool is, if it is applied to a wrong problem, it may delay problem solving, or even worse, cause harm. Experts are needed to match tools with problems. They are not only just domain experts who know a lot about the problems they are facing, but experts who are familiar with feature selection methods. If it is not impossible for us to have such experts, these experts are rare to find. The matching problem still exists regardless of whether we can find such experts or not. The second best solution is to abstract both tools and problems. By relating tools with problems, hopefully, we can help solve the matching problem. Abstracting problems can be done by domain experts according to characteristics of data; abstracting tools requires some measures that can tell us how good each method is and under what circumstances it is good. In order to wisely apply feature selection methods, we need to first discuss their performance.
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Liu, H., Motoda, H. (1998). Evaluation and Application. In: Feature Selection for Knowledge Discovery and Data Mining. The Springer International Series in Engineering and Computer Science, vol 454. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5689-3_5
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DOI: https://doi.org/10.1007/978-1-4615-5689-3_5
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