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## Abstract:Abstract-The paper presents the results of research related to the efficiency of the so called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction. The stages of rule growing and pruning were considered along with the issue of conflicts resolution which may occur during the classification. The work is the continuation of research on the efficiency of quality measures employed in sequential covering rule induction algorithm. In this paper we analyse only the… Show more

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##### Cited by 9 publications
(7 citation statements)
##### References 41 publications
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“…Because the classification rule set varies with rule evaluation functions, each rule evaluation function owns a group of artificial ants, a construction graph and a rule set. References [11][12][13] summarize some common rule evaluation functions, such as Klosgen measure, F-measure and M-estimate. We select four rule evaluation functions with better performance as candidate rule evaluation functions in A_HACO, which are Klosgen measure, F-measure, M-estimate and Q + .…”
Section: Rule Generatingmentioning
confidence: 99%
See 3 more Smart Citations
“…Because the classification rule set varies with rule evaluation functions, each rule evaluation function owns a group of artificial ants, a construction graph and a rule set. References [11][12][13] summarize some common rule evaluation functions, such as Klosgen measure, F-measure and M-estimate. We select four rule evaluation functions with better performance as candidate rule evaluation functions in A_HACO, which are Klosgen measure, F-measure, M-estimate and Q + .…”
Section: Rule Generatingmentioning
confidence: 99%
“…The probability with the food source Xi to be chosen by an onlooker is defined by (11). (11) Given the food source Xi, local search strategy to find a neighbor food source Vi for employed bees or onlookers is defined by (12…”
Section: Artificial Bee Colony Optimization Processmentioning
confidence: 99%
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“…Further research is done by M.,Michalak, et al, which also builds measurement metrics against rule quality induced by algorithms [18]. The measurement metrics selected and used in this experiment are as follows:…”
Section: Figure 2 Taxonomic Measurement Of Interestingness Rulementioning
confidence: 99%