scholarly journals Fundations of Decision Rule Induction: Covering Algorithms

2018 ◽  
Vol 30 (4) ◽  
pp. 205-208
Author(s):  
Yoshifumi KUSUNOKI
Keyword(s):  
Decision Rule ◽  
Rule Induction ◽  
Covering Algorithms

scholarly journals Decision Rule Induction: Relieving Complexity in Detecting Defection

2019 ◽  
Vol 8 (2) ◽  
pp. 3119-3123
Keyword(s):  
Decision Rule ◽  
Sensitivity And Specificity ◽  
Predictive Accuracy ◽  
Decision Rules ◽  
Rule Induction ◽  
Telecom Service ◽  
Customer Attrition

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

Role of Robustness Measure in Rule Induction

2016 ◽  
Vol 20 (4) ◽  
pp. 580-589 ◽  
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.

Dominance-based decision rule induction for multicriteria ranking

2012 ◽  
Vol 4 (5) ◽  
pp. 427-444 ◽  
Author(s):  
Junyi Chai ◽  
James N. K. Liu
Keyword(s):  
Decision Rule ◽  
Rule Induction

scholarly journals A Hyper-Heuristic for Descriptive Rule Induction

2008 ◽  
pp. 3164-3175
Author(s):  
Tho Hoan Pham ◽  
Tu Bao Ho
Keyword(s):  
Microarray Data ◽  
Rule Induction ◽  
Exhaustive Search ◽  
Relative Accuracy ◽  
Divide And Conquer ◽  
The Third ◽  
Search Heuristics ◽  
Linear Rule ◽  
Non Linear ◽  
Covering Algorithms

There are in general three approaches to rule induction: exhaustive search, divide-and conquer, and separate-and-conquer (or its extension as weighted covering). Among them, the third approach, according to different rule search heuristics, can avoid the problem of producing many redundant rules (limitation of the first approach) or non-overlapping rules (limitation of the second approach). In this chapter, we propose a hyper-heuristic to construct rule search heuristics for weighted covering algorithms that allows producing rules of desired generality. The hyper-heuristic is based on a PN space, a new ROC-like tool for analysis, evaluation, and visualization of rules. Well-known rule search heuristics such as entropy, Laplacian, weight relative accuracy, and others are equivalent to ones proposed by the hyper-heuristic. Moreover, it can present new non-linear rule search heuristics, some are especially appropriate for description tasks. The non-linear rule search heuristics have been experimentally compared with others on the generality of rules induced from UCI datasets and used to learn regulatory rules from microarray data.

The Role of a Decision Rule in Symptomatic Pulmonary Embolism Patients with a Non-high Probability Ventilation-perfusion Scan

1997 ◽  
Vol 78 (02) ◽  
pp. 794-798 ◽  
Author(s):  
Bowine C Michel ◽  
Philomeen M M Kuijer ◽  
Joseph McDonnell ◽  
Edwin J R van Beek ◽  
Frans F H Rutten ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Decision Rule ◽  
Predictive Value ◽  
Patient Population ◽  
High Probability ◽  
Characteristic Curve ◽  
Stepwise Logistic Regression ◽  
Pulmonary Angiogram ◽  
Symptomatic Pulmonary Embolism ◽  
Perfusion Scan

Summary Background: In order to improve the use of information contained in the medical history and physical examination in patients with suspected pulmonary embolism and a non-high probability ventilation-perfusion scan, we assessed whether a simple, quantitative decision rule could be derived for the diagnosis or exclusion of pulmonary embolism. Methods: In 140 consecutive symptomatic patients with a non- high probability ventilation-perfusion scan and an interpretable pulmonary angiogram, various clinical and lung scan items were collected prospectively and analyzed by multivariate stepwise logistic regression analysis to identify the most informative combination of items. Results: The prevalence of proven pulmonary embolism in the patient population was 27.1%. A decision rule containing the presence of wheezing, previous deep venous thrombosis, recently developed or worsened cough, body temperature above 37° C and multiple defects on the perfusion scan was constructed. For the rule the area under the Receiver Operating Characteristic curve was larger than that of the prior probability of pulmonary embolism as assessed by the physician at presentation (0.76 versus 0.59; p = 0.0097). At the cut-off point with the maximal positive predictive value 2% of the patients scored positive, at the cut-off point with the maximal negative predictive value pulmonary embolism could be excluded in 16% of the patients. Conclusions: We derived a simple decision rule containing 5 easily interpretable variables for the patient population specified. The optimal use of the rule appears to be in the exclusion of pulmonary embolism. Prospective validation of this rule is indicated to confirm its clinical utility.

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