Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector

Vijaya, J.; Sivasankar, E.

doi:10.1007/s00607-018-0633-6

Cited by 34 publications

(24 citation statements)

References 33 publications

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“…Rough set is an important mathematical tool for dealing with inaccurate data. Unlike evidence theory and fuzzy set theory, the rough set theory does not require any prior knowledge or additional information about the data [15]. In the rough set theory, the data table is called the information system.…”

Section: The Discretization Algorithm Based On Breakpoint Discrimination In Rough Setmentioning

confidence: 99%

Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Li¹,

Shen²

2020

Symmetry

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Discretization based on rough sets is used to divide the space formed by continuous attribute values with as few breakpoint sets as possible, while maintaining the original indistinguishable relationship of the decision system, so as to accurately classify and identify related information. In this study, a discretization algorithm for incomplete economic information in rough set based on big data is proposed. First, the algorithm for filling-in incomplete economic information based on deep learning is used to supplement the incomplete economic information. Then, based on breakpoint discrimination, the algorithm for discretization in the rough set is used to implement the discretization based on rough set for supplementary economic information. The performance of this algorithm was tested using multiple sets of data and compared with other algorithms. Experimental results show that this algorithm is effective for discretization based on a rough set of incomplete economic information. When the number of incomplete economic information rough candidate breakpoints increases, it still has a higher computational efficiency and can effectively improve the integrity of incomplete economic information, and finally the application performance is superior.

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Section: The Discretization Algorithm Based On Breakpoint Discrimination In Rough Setmentioning

confidence: 99%

Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Li¹,

Shen²

2020

Symmetry

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show abstract

“…In previous image segmentation techniques, threshold-based segmentation is a basic method. The method is simple and has advantages in processing speed, but it is not suitable for the segmentation of blurred boundary areas in images [11], [12]. For the segmentation of blurred boundary areas, unsupervised clustering method is usually used, and K-means clustering method is commonly used.…”

Section: A Related Workmentioning

confidence: 99%

Brain Image Segmentation Based on FCM Clustering Algorithm and Rough Set

et al. 2019

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In this paper, a new image segmentation method is proposed by combining the FCM clustering algorithm with a rough set theory. First, the attribute value table is constructed based on the segmentation results of FCM under different clustering numbers, and the image is divided into several small regions based on the indistinguishable relationship of attributes. Then, the weight values of each attribute are obtained by value reduction and used as the basis to calculate the difference between regions and then the similarity evaluation of each region is realized through the equivalence relationship defined by the difference degree. Finally, the final equivalence relation defined by similarity is used to merge regions and complete image segmentation. This method is validated in the segmentation of artificially generated images, brain CT images, and MRI images. The experimental results show that compared with the FCM method, the proposed method can reduce the error rate and achieve better segmentation results for the fuzzy boundary region. And, the experimental results also prove that the algorithm has strong anti-noise ability.

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“…The knowledge expression in an if-then format has demonstrated its intuitiveness and clearness. Thus, RST has been widely initiated upon many research domains, such as quality prediction (Yin et al 2019), energy consumption (Cao et al 2020), spam classification (Dutta et al 2018), stock exchange (Joulaei and Mirbolouki 2020), and customer churn prediction (Vijaya and Sivasankar 2018).…”

Section: Introductionmentioning

confidence: 99%

Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment

Hsu

Chen

et al. 2021

Financ Innov

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In a highly intertwined and connected business environment, globalized layout planning can be an effective way for enterprises to expand their market. Nevertheless, conflicts and contradictions always exist between parent and subsidiary enterprises; if they are in different countries, these conflicts can become especially problematic. Internal control systems for subsidiary supervision and management seem to be particularly important when aiming to align subsidiaries’ decisions with parent enterprises’ strategic intentions, and such systems undoubtedly involve numerous criteria/dimensions. An effective tool is urgently needed to clarify the relevant issues and discern the cause-and-effect relationships among them in these conflicts. Traditional statistical approaches cannot fully explain these situations due to the complexity and invisibility of the criteria/dimensions; thus, the fuzzy rough set theory (FRST), with its superior data exploration ability and impreciseness tolerance, can be considered to adequately address the complexities. Motivated by efficient integrated systems, aggregating multiple dissimilar systems’ outputs and converting them into a consensus result can be useful for realizing outstanding performances. Based on this concept, we insert selected criteria/dimensions via FRST into DEMATEL to identify and analyze the dependency and feedback relations among variables of parent/subsidiary gaps and conflicts. The results present the improvement priorities based on their magnitude of impact, in the following order: organizational control structure, business and financial information system management, major financial management, business strategy management, construction of a management system, and integrated audit management. Managers can consider the potential implications herein when formulating future targeted policies to improve subsidiary supervision and strengthen overall corporate governance.

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Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector

Cited by 34 publications

References 33 publications

Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Brain Image Segmentation Based on FCM Clustering Algorithm and Rough Set

Identifying the key factors of subsidiary supervision and management using an innovative hybrid architecture in a big data environment

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