Rule Extraction from Support Vector Machine Using Modified Active Learning Based Approach: An Application to CRM

Farquad, M.A.H.; Ravi, Vadlamani; Raju, S. Bapi

doi:10.1007/978-3-642-15387-7_50

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…[15], proposed a trust framework for an efficient CRM system using multiagent intelligent system. Farquad et al [16,17] described another framework to extract if-then rules from support vector machine for CRM purposes where Finance problems like churn prediction in bank credit cards was used to explain the framework. This paper describes CRM performance measurement based on fuzzy inputs related to maketing, communication, ordered materials…etc.…”

Section: Introductionmentioning

confidence: 99%

Complexity avoidance using biological resemblance of modular multivariable structure

Kouatli¹

2014

2014 10th International Conference on Natural Computation (ICNC)

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Combination of Fuzzy logic and Genetic Algorithm is becoming popular among researchers in the field. GFT (Genetic Fuzzimetric Technique) is of no exception which merges Fuzzy logic with genetic algorithm to achieve the optimization of the decision making process under uncertainty. Multivariable structure of any fuzzy rule based system would add complexity when modeling the behavior of the system. The objective of many researchers in the field is to minimize this complexity and enhance the accuracy. The proposed technique is designed to be a modular approach by resembling biological structure where it avoids the complexity instead of minimizing it while attaining acceptable accuracy of the system. After reviewing the mechanism of GFT, a sample application to measure the CRM performance analysis was used as a vehicle to demonstrate the technique. A generic tool termed as Fuzzy Inference Engine (FIE) was built to demonstrate the multivariable modular approach used to implement the CRM performance measurement.

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Section: Introductionmentioning

confidence: 99%

Complexity avoidance using biological resemblance of modular multivariable structure

Kouatli¹

2014

2014 10th International Conference on Natural Computation (ICNC)

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A SLR on Customer Dropout Prediction

et al. 2022

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Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.

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Analytical CRM in banking and finance using SVM: a modified active learning-based rule extraction approach

Farquad

Ravi

Raju

2012

IJECRM

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Rule Extraction from Support Vector Machine Using Modified Active Learning Based Approach: An Application to CRM

Cited by 4 publications

References 36 publications

Complexity avoidance using biological resemblance of modular multivariable structure

Complexity avoidance using biological resemblance of modular multivariable structure

A SLR on Customer Dropout Prediction

Analytical CRM in banking and finance using SVM: a modified active learning-based rule extraction approach

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