Granular structure-based incremental updating for multi-label classification

Yuanjian, Zhang; Miao, Duoqian; Pedrycz, Witold; Zhao, Tianna; Xu, Jianfeng; Yu, Ying

doi:10.1016/j.knosys.2019.105066

Cited by 18 publications

(10 citation statements)

References 45 publications

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“…Paper [101] remains in the area of ensembles of decision models but it also touches an important aspect of incremental learning in dynamic data environments [102]. Herein, it is worth recalling a gentle difference between reasoning about objects or states in a repetitive fashion (whereby the values of attributes in information systems need to be cyclically updated) and reasoning about temporal objects or phenomena (which require different construction of information systems with attributes reflecting changes and trends) [103], [104].…”

Section: B Rough Set Contest At Pp-rai 2022mentioning

confidence: 99%

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Rough Sets Turn 40: From Information Systems to Intelligent Systems

Skowron¹,

Ślęzak²

2022

Annals of Computer Science and Information Systems

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The theory of rough sets was founded by Zdzisław Pawlak as a framework for data and knowledge exploration. His seminal paper titled "Rough Sets" was published in 1982, in International Journal of Computer and Information Sciences. One of the key aspects that lets us use rough sets in practical scenarios is the notion of information system, which comes from even earlier Professor Pawlak's works. Information systems are the means for data and knowledge representation. They constitute the input to rough set mechanisms aimed at computing approximations of concepts and deriving compacted, interpretable decision models. In particular, the fundamental notion of the indiscernibility relation is defined on the basis of a given information system. Accordingly, we discuss to what extent information systems can serve as the basis for intelligent systems. We claim that in many cases it is not enough to treat a data set -represented as an information system -as a purely abstract object with no linkage to the data origins. Oppositely, we should give ourselves a technical possibility to construct information systems dynamically, taking into account interaction with physical environments where the data comes from. With this respect, we refer to the notions of interactive granular computing and we generally consider together the paradigms of rough sets, information systems, and information granulation.

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Section: B Rough Set Contest At Pp-rai 2022mentioning

confidence: 99%

“…Actually, we have already dealt with some of other "V's" in the previous sections, e.g. Volume [61], Velocity [101] and Variety [60]. However, without addressing Veracity, i.e.…”

Section: A Reliability Of Informationmentioning

confidence: 99%

Rough Sets Turn 40: From Information Systems to Intelligent Systems

Skowron¹,

Ślęzak²

2022

Annals of Computer Science and Information Systems

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“…It simulates the realization of human-centered operations in the presence of multi-faceted data. It contains a large number of techniques to minimize uncertainty [10]. A recognized feature of artificial intelligence is that people can observe and analyze the same problem from extremely different granularities.…”

Section: Important Books Must Be Read Over and Over Again And Every Time You Read It You Will Find It Beneficial To Open The Bookmentioning

confidence: 99%

“…is obtained by averaging 1 in the context direction, as shown in formula (9), to highlight the keywords in the question. And calculate the context key part attention associated with the question keyword to get , as shown in formula (10). As shown in formula (11), highlight the key part of the context and add it to the context vector representation to get the key information-aware context representation.…”

Section: ) Skimming Reading Modulementioning

confidence: 99%

S&I Reader: Multi-Granularity Gated Multi-Hop Skimming and Intensive Reading Model for Machine Reading Comprehension

2021

Self Cite

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Machine reading comprehension is a very challenging task, which aims to determine the answer span based on the given context and question. The newly developed pre-training language model has achieved a series of successes in various natural language understanding tasks with its powerful contextual representation ability. However, these pre-training language models generally lack the downstream processing structure for specific tasks, which limits further performance improvement. In order to solve this problem and deepen the model's understanding of the question and context, this paper proposes S&I Reader. On the basis of the pre-training model, skimming, intensive reading, and gated mechanism modules are added to simulate the behavior of humans reading text and filtering information. Based on the idea of granular computing, a multi-granularity module for computing context granularity and sequence granularity is added to the model to simulate the behavior of human beings to understand the text from words to sentences, from parts to the whole. Compared with the previous machine reading comprehension model, our model structure is novel. The skimming module and multi-granularity module proposed in this paper are used to solve the problem that the previous model ignores the key information of the text and cannot understand the text with multi granularity. Experiments show that the model proposed in this paper is effective for both Chinese and English datasets. It can better understand the question and context and give a more accurate answer. The performance has made new progress on the basis of the baseline model.INDEX TERMS Gated mechanism, granular computing, intensive reading, machine reading comprehension, pre-training model, skimming.

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“…In the process of using Granular Computing to deal with problems, information granulation [20]- [23] is a crucial step. The samples in data are combined into a collection of information granules by knowledge [24], [25] and their association degree, which can further form a granular structure [26]- [28]. If the equivalence relation [15] is employed, then information granulation is actually the processes of dividing samples by equivalence relation and obtaining equivalent granular structure; if the neighborhood relation [32], [33] is employed, then information granulation is actually the processes of generating the neighborhood of each sample and obtaining neighborhood granular structure.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Granularity for Continuous Parameters

She

et al. 2021

IEEE Access

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In the community of Granular Computing, knowledge is interpreted as one classification ability of realistic or abstract objects. Generally, the concept of granularity is used for characterizing such an ability, which has been widely explored in literatures. To calculate the parameterized granularity, a naive approach is to find the granularity in terms of the parameter one by one. Nevertheless, such approach can only generate the single parameter based knowledge granularity, and the difference of knowledge granularities among different parameters may be slight. It follows that the knowledge granularity derived from single parameter may be lack of representativeness. In this paper, the continuous parameters based knowledge granularity is proposed, and the corresponding calculation approach is presented. Inspired by the thinking of definite integral in mathematical problems, the calculation approach is mainly implemented by following steps: firstly, the graph formed by granularity and parameter interval is divided into several small rectangles whose length of interval tends to be 0; secondly, the sum of area values of all the small rectangles is calculated; finally, the obtained area value divided by the whole length of parameter interval can be considered as the continuous parameters based knowledge granularity. This study suggests a new trend of handling problems related to knowledge from the viewpoint of continuity.

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Granular structure-based incremental updating for multi-label classification

Cited by 18 publications

References 45 publications

Rough Sets Turn 40: From Information Systems to Intelligent Systems

Rough Sets Turn 40: From Information Systems to Intelligent Systems

S&I Reader: Multi-Granularity Gated Multi-Hop Skimming and Intensive Reading Model for Machine Reading Comprehension

Knowledge Granularity for Continuous Parameters

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