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    Comparison of some classification algorithms based on deterministic and nondeterministic decision rules

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    Type
    Conference Paper
    Authors
    Delimata, Paweł
    Marszał-Paszek, Barbara
    Moshkov, Mikhail cc
    Paszek, Piotr
    Skowron, Andrzej
    Suraj, Zbigniew
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Applied Mathematics and Computational Science Program
    Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
    Date
    2010
    Permanent link to this record
    http://hdl.handle.net/10754/564258
    
    Metadata
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    Abstract
    We discuss two, in a sense extreme, kinds of nondeterministic rules in decision tables. The first kind of rules, called as inhibitory rules, are blocking only one decision value (i.e., they have all but one decisions from all possible decisions on their right hand sides). Contrary to this, any rule of the second kind, called as a bounded nondeterministic rule, can have on the right hand side only a few decisions. We show that both kinds of rules can be used for improving the quality of classification. In the paper, two lazy classification algorithms of polynomial time complexity are considered. These algorithms are based on deterministic and inhibitory decision rules, but the direct generation of rules is not required. Instead of this, for any new object the considered algorithms extract from a given decision table efficiently some information about the set of rules. Next, this information is used by a decision-making procedure. The reported results of experiments show that the algorithms based on inhibitory decision rules are often better than those based on deterministic decision rules. We also present an application of bounded nondeterministic rules in construction of rule based classifiers. We include the results of experiments showing that by combining rule based classifiers based on minimal decision rules with bounded nondeterministic rules having confidence close to 1 and sufficiently large support, it is possible to improve the classification quality. © 2010 Springer-Verlag.
    Citation
    Delimata, P., Marszał-Paszek, B., Moshkov, M., Paszek, P., Skowron, A., & Suraj, Z. (2010). Comparison of Some Classification Algorithms Based on Deterministic and Nondeterministic Decision Rules. Transactions on Rough Sets XII, 90–105. doi:10.1007/978-3-642-14467-7_5
    Publisher
    Springer Nature
    Journal
    Lecture Notes in Computer Science
    Conference/Event name
    Rough Set and Knowledge Technology Conference, RSKT 2008
    ISBN
    3642144667; 9783642144660
    DOI
    10.1007/978-3-642-14467-7_5
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-14467-7_5
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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