Using association rules to make rule-based classifiers robust
Rule-based classification systems have been widely used in real world applications
because of the easy interpretability of rules. Many traditional rule-based classifiers prefer
small rule sets to large rule sets, but small classifiers are sensitive to the missing values in
unseen test data. In this paper, we present a larger classifier that is less sensitive to the
missing values in unseen test data. We experimentally show that it is more accurate than
some benchmark classifies when unseen test data have missing values.
because of the easy interpretability of rules. Many traditional rule-based classifiers prefer
small rule sets to large rule sets, but small classifiers are sensitive to the missing values in
unseen test data. In this paper, we present a larger classifier that is less sensitive to the
missing values in unseen test data. We experimentally show that it is more accurate than
some benchmark classifies when unseen test data have missing values.
Rule-based classification systems have been widely used in real world applications because of the easy interpretability of rules. Many traditional rule-based classifiers prefer small rule sets to large rule sets, but small classifiers are sensitive to the missing values in unseen test data. In this paper, we present a larger classifier that is less sensitive to the missing values in unseen test data. We experimentally show that it is more accurate than some benchmark classifies when unseen test data have missing values.
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