An Incremental Learning Approach for Updating Approximations in Rough Set Model over Dual Universes

Hu, Jie; Li, Tianrui; Chen, Hongmei; Zeng, An‐Ping

doi:10.1002/int.21732

Cited by 12 publications

(4 citation statements)

References 38 publications

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“…Chen [6] proposed a multi-criterion decision approximation update algorithm based on index blocks when objects are added or removed. Hu [7] proposed an incremental method for updating the approximations of a two-domain rough set when the objects are changed over time. Chen[8] discussed the information granules and approximations properties of objects in a dynamic environment with time change.…”

Section: Introductionmentioning

confidence: 99%

Approximation maintenance when updating conditional values in set-valued ordered decision systems

Wang,

2023

Preprint

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The integration of set-valued ordered rough set models and incremental learning signifies a progressive advancement of conventional rough set theory, with the objective of tackling the heterogeneity and ongoing transformations in information systems. In set-valued ordered decision systems, when changes occur in the attribute value domain, such as adding or removing conditional values, it may result in changes in the preference relation between objects, indirectly leading to changes in approximations. In this paper, we effectively address the issue of updating approximations that arise from adding or removing conditional values in set-valued ordered decision systems. Firstly, we classify the research objects into two categories: objects with changes in conditional values and objects without changes, and then conduct theoretical studies on updating approximations for these two categories, presenting approximation update theories for adding or removing conditional values. Subsequently, we present incremental algorithms corresponding to approximation update theories. We demonstrate the feasibility of the proposed incremental update method with numerical examples and show that our incremental algorithm outperforms the static algorithm. Ultimately, by comparing experimental results on different datasets, it is evident that the incremental algorithm efficiently reduces processing time. In conclusion, this study offers a promising strategy to address the challenges of set-valued ordered decision systems in dynamic environments.

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Section: Introductionmentioning

confidence: 99%

Approximation maintenance when updating conditional values in set-valued ordered decision systems

Wang,

2023

Preprint

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“…An incremental updating algorithm of approximate set based on the dominance relationship in ordered information system was proposed by Li et al [11]. Considering the data analysis of dual universes, Hu et al studied the dynamic updating approximations [12]. Luo et al explored an efficient updating approach of probabilistic rough set with incremental objects [13].…”

Section: Introductionmentioning

confidence: 99%

A dynamic framework for updating approximations with increasing or decreasing objects in multi-granulation rough sets

Hong

Guan

2021

Preprint

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The data we need to deal with is getting bigger and bigger in recent years, and the same happens to multi-granulation rough set, so updated schemes have been proposed with the variation of attributes or attribute values in multi-granulation rough sets, this paper puts forward a dynamic mechanism to update the approximations of multi-granulation rough sets when adding or deleting objects. Firstly, the relationships between the original approximations and updated approximations are explored when adding or deleting objects in multi-granulation rough sets, and the dynamic processes of updating optimistic and pessimistic multi-granulation rough approximations are proposed. Secondly, two corresponding dynamic algorithms are proposed to update the lower and upper approximations of optimistic and pessimistic multi-granulation rough sets. Finally, a great quantity of experiments had been implemented, and the results indicate that two dynamic algorithms proposed are more effective than the static algorithm.

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“…In our previous work [65], we have investigated the incremental update of rough approximations in ISTU when single object of two universes with a crisp binary relation varies with time. Nevertheless, ISTU with a fuzzy relation is more common in real-life applications than ISTU with a crisp relation, e.g., recommender systems [66], fuzzy multiple criteria decision making [22] and clinical diagnosis systems [25,67].…”

Section: Introductionmentioning

confidence: 99%

“…And in general, the objects in the fuzzy ISTU are added in or deleted simultaneously in real situations. The existing method [65] processing the multiple objects' variation one by one is inefficient since it has to be executed repeatedly, especially when a large number of objects enter in or get out from the fuzzy ISTU. On the other hand, the FPRSMTU deals with a fuzzy relation (incompatible or compatible) and allows a tolerance inaccuracy in the construction of rough approximations in a fuzzy ISTU, and thus has a wider range of applications.…”

Section: Introductionmentioning

confidence: 99%

Incremental fuzzy probabilistic rough sets over two universes

Luo

et al. 2017

International Journal of Approximate Reasoning

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The fuzzy Information System over Two Universes (ISTU) formalizing a data table corresponding to two universes as well as their relations is common in real-world applications, e.g., recommender system and clinical diagnosis system. The fuzzy probabilistic rough sets over two universes (FPRSMTU) can deal with a fuzzy relation and allow a tolerance inaccuracy in the construction of rough approximations in a fuzzy ISTU, which is a generalization of classic rough sets with fuzzy and probabilistic theories. As a necessary step for knowledge discovery based on rough sets, the fuzzy rough approximations of fuzzy ISTU need to be updated efficiently under dynamic data environment. Incremental technique is an efficient approach for dynamic information processing by making full use of previously obtained knowledge. In this paper, incremental approaches for updating approximations of fuzzy ISTU are proposed while some objects adding into or deleting from the two universes, and the corresponding incremental algorithms are designed. Experimental evaluations on real datasets as well as artificial datasets show the effectiveness of the proposed incremental updating method compared with the non-incremental method.

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An Incremental Learning Approach for Updating Approximations in Rough Set Model over Dual Universes

Cited by 12 publications

References 38 publications

Approximation maintenance when updating conditional values in set-valued ordered decision systems

Approximation maintenance when updating conditional values in set-valued ordered decision systems

A dynamic framework for updating approximations with increasing or decreasing objects in multi-granulation rough sets

Incremental fuzzy probabilistic rough sets over two universes

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