Decision Rule Induction Based on the Graph Theory

Customer attrition has become a serious problem globally, particularly in telecom service, resulting into substantial revenue decline. Attrition may result in accumulation ofdues as a resultof payment defaults.Proactive identification of potential attrite will help in retention as well as minimizing loss of revenue.For attrition detection many robust but complex algorithms are used. Depending on the severity of error, the complexity can be lessened and thus cost. Two methods of decision rules (1R& C5.0) are used to predict the attrition and predictive accuracy is judged withconfusion matrix. Comparison between models is made by sensitivity and specificity. It was found that 1R has a sensitivity of .60 against .69 for C5.0 and hence, the performance is not significantly different. It is suggested that 1R could be used instead of more complex algorithmsand also it can be adopted for benchmarking

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A Comparative Study on Decision Rule Induction for incomplete data using Rough Set and Random Tree Approaches

IOSR Journal of Computer Engineering ◽

10.9790/0661-0930610 ◽

2013 ◽

Vol 9 (3) ◽

pp. 6-10

Author(s):

M. Sandhya

Keyword(s):

Comparative Study ◽

Decision Rule ◽

Rough Set ◽

Incomplete Data ◽

Rule Induction ◽

Random Tree

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Role of Robustness Measure in Rule Induction

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2016.p0580 ◽

2016 ◽

Vol 20 (4) ◽

pp. 580-589 ◽

Cited By ~ 2

Author(s):

Motoyuki Ohki ◽

◽

Eiji Sekiya ◽

Masahiro Inuiguchi

Keyword(s):

Decision Rule ◽

Rough Set ◽

Numerical Experiments ◽

Decision Rules ◽

Rule Induction ◽

Evaluation Index ◽

Design Knowledge ◽

Robustness Measure ◽

New Type

Rough set approaches provide useful tools to induce minimal decision rules from given data. Acquired minimal rules are typically used to build a classifier. However, minimal rules are sometimes used for design knowledge. Specifically, if a new object is designed to satisfy the condition of a minimal rule, it can be classified into a class suggested by the rule. Although we are interested in the goodness of the set of obtained minimal decision rules for the purpose of building a classifier, we are more interested in the goodness of an individual minimal decision rule for design knowledge. In this study, we propose robustness measures as a new type of evaluation index for decision rules. The measure evaluates the extent to which interestingness is preserved after the some conditions are removed. Four numerical experiments are conducted to examine the usefulness of robusetness measures. Decision rules selected by robustness scores are compared with those selected by recall, which is the well-known measure to select good rules. Our results reveal that a different aspect of the goodness of a rule is evaluated by the robustness measure and thus, the robustness measure acts as an independent and complementary index of recall.

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