Abstract
A method is presented to induce decision rules from data with missing values where (a) the format of the rules is no different than rules for data without missing values and (b) no special features are spe- cified to prepare the the original data or to apply the induced rules. This method generates compact Disjunctive Normal Form (DNF) rules. Each class has an equal number of unweighted rules. A new example is classi- fied by applying all rules and assigning the example to the class with the most satisfied rules. Disjuncts in rules are naturally overlapping. When combined with voted solutions, the inherent redundancy is enhanced. We provide experimental evidence that this transparent approach to classi- fication can yield strong results for data mining with missing values.
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Weiss, S.M., Indurkhya, N. (2000). Decision-Rule Solutions for Data Mining with Missing Values. In: Monard, M.C., Sichman, J.S. (eds) Advances in Artificial Intelligence. IBERAMIA SBIA 2000 2000. Lecture Notes in Computer Science(), vol 1952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44399-1_1
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DOI: https://doi.org/10.1007/3-540-44399-1_1
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