Incremental updating of rough approximations in interval-valued information systems under attribute generalization

Zhang, Yingying; Li, Tianrui; Luo, Chuan; Chen, Hongmei

doi:10.1016/j.ins.2016.09.018

Cited by 60 publications

(17 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dai et al [8] introduced uncertainty measurement for an IVIS based on α-weak similarity. Zhang et al [46] presented incremental updating of rough approximations in an IVIS under attribute generalization. Leung et al [19] brought up the minimal attribute reduction method for an IVIS and obtained all classification rules hidden in an IS through a knowledge induction process.…”

Section: A Research Background and Related Workmentioning

confidence: 99%

Feature Selection for Interval-Valued Data Based on D-S Evidence Theory

Peng

Zhang

2021

IEEE Access

View full text Add to dashboard Cite

Feature selection is one basic and critical technology for data mining, especially in current "big data era". Rough set theory (RST) is sensitive to noise in feature selection due to the strict condition of equivalence relation. However, D-S evidence theory is flexible to measure uncertainty of information. This paper introduces robust feature evaluation metrics "belief function" and "plausibility function" into feature selection algorithm to avoid the defect that classification effect is affected by noise. First of all, similarity between information values in an interval-valued information system (IVIS) is given and a variable parameter to control the similarity of samples is introduced. Then, θ-lower approximation and θ-upper approximation in IVIS are put forward. Next, belief function and plausibility function based on θlower approximation and θ-upper approximation are put forward. Finally, four feature selection algorithms in an IVIS based on D-S evidence theory are proposed. The experimental results on four real interval-valued datasets show that the proposed metrics are robust to noise, and the proposed algorithms are more effective than the existing algorithms.

show abstract

Section: A Research Background and Related Workmentioning

confidence: 99%

Feature Selection for Interval-Valued Data Based on D-S Evidence Theory

Peng

Zhang

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In recent years, many scholars have made outstanding achievements in the research of interval-valued data. Zhang et al [42] presented incremental updating of rough approximations in an intervalvalued information system (IVIS) under attribute generalization. Dai et al [9] introduced uncertainty measurement for an incomplete interval-valued information system (IIVIS) based on α-weak similarity.…”

Section: Introduction a Research Background And Related Workmentioning

confidence: 99%

Attribute Reduction in an Incomplete Interval-Valued Decision Information System

Chen

Zhang

2021

IEEE Access

View full text Add to dashboard Cite

An incomplete interval-valued decision information system (IIVDIS) is a significant type of data decision table, which is ubiquitous in real life. Interval value is a form of knowledge representation, and it seems to be an embodiment of the uncertainty of research objects. In this paper, we focus on attribute reduction on the basis of a parameterized tolerance-based rough set model in an IIVDIS. Firstly, we give the similarity degree between information values on each attribute in an IIVDIS by considering incomplete information. Then, we present tolerance relations on the object set of an IIVDIS based on this similarity degree. Next, we define the rough approximations by means of the presented tolerance relation. Based on Kryszkiewicz's ideal, we introduce α-generalized decision and consider attribute reduction in an IIVDIS by means of this decision. Furthermore, we put forward the notions of α-information entropy, α-conditional information entropy and α-joint information entropy in an IIVDIS. And we prove that α-positive region reduction theorem, α-conditional entropy reduction theorem, α-dependency reduction theorem and α-generalized decision reduction theorem are equivalent to each other. Finally, we propose two attribute reduction methods in an IIVDIS by using entropy measurement and the rough approximations, and design the relevant algorithms. We carry out a series of numerical experiments to verify the effectiveness of the proposed algorithms. The experimental results show that proposed algorithms often choose fewer attributes and improve classification accuracies in most cases.

show abstract

“…(2) incremental knowledge update while the attribute set varies [18][19][20][21][22][23]; (3) incremental knowledge update while the attribute values vary [24][25][26][27]; (4) incremental knowledge update under the simultaneous variations of object set and attribute set (or object set and attribute value, attribute set and attribute value) [28][29][30][31][32]. All these researches will help decision makers update knowledge from different perspectives in different kinds of ISs.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Knowledge Update Using Three-Way Decisions in Dominance-Based Rough Sets Approach While the Object Set Varies

Wang¹,

Li²,

Ye³

et al. 2019

IJCIS

View full text Add to dashboard Cite

Dominance-based rough set approach is the extension of classical Pawlak rough set theories and methodologies, in which the information with preference-ordered relation on the domain of attribute value is fully considered. In the dominance-based information system, upper and lower approximations will be changed while the object set varies over time and the approximations need to be updated correspondingly for their variations result in changes of knowledge and rules. Considering that three-way decisions is a special class of general and effective human ways of problem solving and information processing, a new incremental maintenance mechanism using three-way decisions is proposed in this paper, namely, the universe is divided into three pair-wise disjoint subsets firstly, then appropriate strategies are developed and acted on each subsets. Furthermore, the corresponding methods for updating the approximations of upward unions and downward unions of decision classes are analyzed systematically under the variations of object set in the dominance-based information system from the perspective of three-way decisions. Moreover, considering vector representation and calculation is intuitive and concise, two incremental update algorithms of approximations are suggested and implemented in the MATLAB platform. Finally, some tests on data sets from UCI (UC Irvine Machine Learning Repository) are undertaken to verify the effectiveness of the proposed methods. Compared with the non-incremental updating methods, the proposed incremental updating method with three-way decisions generally exhibits a better performance.

show abstract

Incremental updating of rough approximations in interval-valued information systems under attribute generalization

Cited by 60 publications

References 48 publications

Feature Selection for Interval-Valued Data Based on D-S Evidence Theory

Feature Selection for Interval-Valued Data Based on D-S Evidence Theory

Attribute Reduction in an Incomplete Interval-Valued Decision Information System

Dynamic Knowledge Update Using Three-Way Decisions in Dominance-Based Rough Sets Approach While the Object Set Varies

Contact Info

Product

Resources

About