Business Aviation Decision-Making Using Rough Sets

Yang, Yuping; Shieh, How‐Ming; Tzeng, Gwo‐Hshiung; Yen, Leon; Chan, Chien-Chung

doi:10.1007/978-3-540-88425-5_34

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…Many studies have adopted the RST approach to extract rules and patterns from collected data or unclassified information, such as the evaluation of bankruptcy risk [27], business failure prediction [1], travel demand analysis [7], mining stock prices [31], personal investment portfolios [25], accident prevention [32], and R&D performance [29]. The basic concepts of RST are described in the studies by Pawlak [20][21][22][23][24], Ou Yang et al [14], and Wang et al [29].…”

Section: The Basic Concepts Of Rough Setmentioning

confidence: 99%

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Improving airline service quality based on rough set theory and flow graphs

Liou

Chuang

Hsu

2015

Journal of Industrial and Production Engineering

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This study differs from previous studies by applying multivariate statistical analysis and multi-criterion decision-making methods to the improvement of service quality. We use the rough set theory (RST) with a flow graph approach to determine customer attitudes regarding service quality, which can assist managers in developing strategies to improve service quality and thus satisfy the needs of customers. A set of rules is derived from a large sample of airline customers, and its predictive ability is evaluated. The flow graph and the cause-and-effect relationship of the decision rules are heavily exploited in service quality characteristics. As compared with the results of other data-mining analyses, our results are encouraging. This study demonstrates that the combination of the RST model and flow graphs assists in identifying the needs of customers, determining their characteristics, and facilitating the development of an improvement strategy.

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Section: The Basic Concepts Of Rough Setmentioning

confidence: 99%

“…Table 5 shows the set of minimal covering rules that we have generated using only service attributes. The rules are divided into two classes: "very poor" rules (rules 1-7) and "very good" rules (rules [8][9][10][11][12][13][14][15]. The antecedents to the very poor class of rules indicate an attribute rating that the airline should avoid.…”

Section: 2mentioning

confidence: 99%

Improving airline service quality based on rough set theory and flow graphs

Liou

Chuang

Hsu

2015

Journal of Industrial and Production Engineering

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“…With respect to rough set theory, the advantages are they do not require any preliminary or additional parameter about the data, less expensive or time to generate rules, ability to handle large amounts data, yield understandable decision rules and stable [31]- [33]. It can be used to make decisions in any underlying business [42]. In the experimental results of [7], accuracy rate 1is more in LEM2 when compared to DT and ANN.…”

Section: Rule Induction Algorithmsmentioning

confidence: 99%

Customer Behavior Analysis Using Rough Set Approach

Dhandayudam¹,

Krishnamurthi²

2013

J. theor. appl. electron. commer. res.

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“…The FG is even applicable to making decision about deregulation of aviation market. Such solutions have been introduced, for instance, by the government of Taiwan (Ou Yang et al 2008).…”

Section: Related Workmentioning

confidence: 99%

Employing flowgraphs for forward route reconstruction in video surveillance system

Czyżewski

Lisowski

2013

J Intell Inf Syst

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Pawlak's flowgraphs were utilized as a base idea and knowledge container for prediction and decision making algorithms applied to experimental video surveillance system. The system is used for tracking people inside buildings in order to obtain information about their appearance and movement. The fields of view of the cameras did not overlap. Therefore, when an object was moving through unsupervised areas, prediction was needed to identify the same object in the adjacent camera. Moreover, while being unobserved, the object was represented by measurements of probability of appearing in the subsequent camera's field of view. Those probability values were obtained on the basis of knowledge from the past events contained in the flowgraph. Backward Route Reconstruction methods generated paths of objects providing input for prediction of next steps in the path. This prediction was named as Forward Route Reconstruction. The output of the prediction algorithm is a tree of probabilities of future object movement. Methods for creating a flowgraph from the paths of objects and for obtaining probability values for movement prediction are presented in this paper together with some experimental results and discussion.

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Business Aviation Decision-Making Using Rough Sets

Cited by 10 publications

References 9 publications

Improving airline service quality based on rough set theory and flow graphs

Improving airline service quality based on rough set theory and flow graphs

Customer Behavior Analysis Using Rough Set Approach

Employing flowgraphs for forward route reconstruction in video surveillance system

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