Yao Qi, Raja Das, et al.
ISSTA 2009
Generating classification rules or decision trees from examples has been a subject of intense study in the pattern recognition community, the statistics community, and the machine-learning community of the artificial intelligence area. We pursue a point of view that minimality of rules is important, perhaps above all other considerations (biases) that come into play in generating rules. We present a new minimal rule-generation algorithm called R-MINI (Rule-MINI) that is an adaptation of a well-established heuristic-switching-function-minimization technique, MINI. The main mechanism that reduces the number of rules is repeated application of generalization and specialization operations to the rule set while maintaining completeness and consistency. R-MINI results on some benchmark cases are also presented. © 1997 IEEE.
Yao Qi, Raja Das, et al.
ISSTA 2009
Israel Cidon, Leonidas Georgiadis, et al.
IEEE/ACM Transactions on Networking
Michael C. McCord, Violetta Cavalli-Sforza
ACL 2007
Preeti Malakar, Thomas George, et al.
SC 2012