The development of granular rule-based systems: a study in structural model compression

Ahmad, Sharifah Sakinah Syed; Pedrycz, Witold

doi:10.1007/s41066-016-0022-5

Cited by 42 publications

(7 citation statements)

References 20 publications

(20 reference statements)

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“…In addition, we will investigate the adoption of Prism-based rule learning for multi-class classification tasks. In particular, we will identify more effective ways for the selection of the target class for learning each single rule of as high quality as possible, in the setting of granular computing Liu and Cocea 2018a;Liu et al 2016d;Ahmad and Pedrycz 2017). Furthermore, it is worth to adopt fuzzy set theory for fuzzification of continuous attributes (Chen 1996;Chen et al 2014;Mendel et al 2006;Lee and Chen 2008;Chen and Lee 2010) for improving the quality of rules, and employ optimization techniques for searching an optimal set of rules in terms of rule quality (Chen and Chung 2006;Tsai et al 2008Tsai et al , 2012.…”

Section: Discussionmentioning

confidence: 99%

Granular computing-based approach of rule learning for binary classification

Liu

Cocea

2018

Granul. Comput.

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Rule learning is one of the most popular types of machine-learning approaches, which typically follow two main strategies: 'divide and conquer' and 'separate and conquer'. The former strategy is aimed at induction of rules in the form of a decision tree, whereas the latter one is aimed at direct induction of if-then rules. Due to the case that the divide and conquer strategy could result in the replicated sub-tree problem, which not only leads to overfitting but also increases the computational complexity in classifying unseen instances, researchers have thus been motivated to develop rule learning approaches through the separate and conquer strategy. In this paper, we focus on investigation of the Prism algorithm, since it is a representative one that follows the separate and conquer strategy, and is aimed at learning a set of rules for each class in the setting of granular computing, where each class (referred to as target class) is viewed as a granule. The Prism algorithm shows highly comparable performance to the most popular algorithms, such as ID3 and C4.5, which follow the divide and conquer strategy. However, due to the need to learn a rule set for each class, Prism usually produces very complex rule-based classifiers. In real applications, there are many problems that involve one target class only, so it is not necessary to learn a rule set for each class, i.e., only a set of rules for the target class needs to be learned and a default rule is used to indicate the case of non-target classes. To address the above issues of Prism, we propose a new version of the algorithm referred to as PrismSTC, where 'STC' stands for 'single target class'. Our experimental results show that PrismSTC leads to production of simpler rule-based classifiers without loss of accuracy in comparison with Prism. PrismSTC also demonstrates sufficiently good performance comparing with C4.5.

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Section: Discussionmentioning

confidence: 99%

Granular computing-based approach of rule learning for binary classification

Liu

Cocea

2018

Granul. Comput.

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“…On the other hand, we will combine the granular computing techniques [32][33][34][35][36] with our developed method to solve real-life MCDM problems, such as the evaluation of green supply chain initiatives.…”

Section: Discussionmentioning

confidence: 99%

Hesitant Trapezoidal Fuzzy QUALIFLEX Method and Its Application in the Evaluation of Green Supply Chain Initiatives

Zhang

Liu

2016

Sustainability

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This paper explores how to handle multiple criteria decision-making (MCDM) problems in which the criteria values of alternatives take the form of comparative linguistic expressions. Firstly, the new concept of hesitant trapezoidal fuzzy numbers (HTrFNs) is provided to model the semantics of the comparative linguistic expressions. Then, the operational laws and the distance measures of HTrFNs are presented. Afterwards, a useful outranking method, the hesitant trapezoidal fuzzy QUALIFLEX method, is developed to handle the MCDM problems with hierarchical structure in the environment of HTrFN. At length, the proposed method is applied to evaluating green supply chain initiatives in order to achieve sustainable economic and environmental performance, and a case study concerned with a fashion retail chain is presented to demonstrate its feasibility and applicability, also, a comparative analysis with other relevant approaches is conducted to validate the effectiveness of the proposed method.

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“…Recently, granular computing is becoming popular to deal with the human-data (see Peters and Weber [26], Livi and Sadeghian [21], Skowron, Jankowski and Dutta [27], and Wilke and Portmann [28]). Liu, Gegov and Cocea [19], and Ahmad and Pedrycz [1] studied the rule-based systems by using granular computing. Maciel, Ballini and Gomide [23] made a granular analytics for interval time series forecasting.…”

Section: Valuing Stock Loan For General Uncertain Stock Modelmentioning

confidence: 99%

Valuation of stock loan under uncertain mean-reverting stock model

Shi

Zhang

Sheng

2017

IFS

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Stock loan is different from the traditional loan, it needs to be collateralized by stock. Fairly valuing stock loan is very important for financial market participants. The main contribution of this paper is to give a valuing method of stock loan in uncertain environment. Under the assumption that the underlying stock price follows an uncertain mean-reverting stock model, the price formulas of standard stock loan and capped stock loan are derived by using the method of uncertain calculus. Some numerical examples are presented to illustrate the related results.

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The development of granular rule-based systems: a study in structural model compression

Cited by 42 publications

References 20 publications

Granular computing-based approach of rule learning for binary classification

Granular computing-based approach of rule learning for binary classification

Hesitant Trapezoidal Fuzzy QUALIFLEX Method and Its Application in the Evaluation of Green Supply Chain Initiatives

Valuation of stock loan under uncertain mean-reverting stock model

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