On the Local Reduction of Information System

Chen, Degang; Tsang, Eric W. K.

doi:10.1007/11739685_61

Cited by 3 publications

(3 citation statements)

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“…Several concepts of local reduction [31]- [34] have been proposed, but they study attribute reduction from the perspective of equivalence classes. To date, there has been a lack of analysis from the perspective of the relation between any two objects.…”

Section: Local Indiscernibility Relation Reduction For Information Ta...mentioning

confidence: 99%

“…However, class-specific attribute reduction provides a special form of classification-based reduction. Chen [34] obtained more optimized local rules by using a local reduction algorithm in a decision table. Considering the decision-making subset, the binary relation has been extended to a fuzzy relation on the discernibility matrix-based reduction algorithm, and the X-lower and X-upper approximation reductions have been derived in the fuzzy relation decision system [35].…”

Section: Introductionmentioning

confidence: 99%

“…Based on the local decision set [31]- [34], [42], [43], this paper analyzes the discernibility relation of any objects in the decision subset. This concept is applicable to communication and consultation [44] in the fields of labor arbitration, diplomatic negotiation, and so on.…”

Section: Introductionmentioning

confidence: 99%

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Local Indiscernibility Relation Reduction for Information Tables

Tang

Tan

2022

IEEE Access

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Attribute reduction comes from machine learning and is an important component of rough set theory. Research on attribute reduction has produced many important achievements. The aim of attribute reduction is to reduce the complexity of data while retaining its original characteristics to the greatest extent. The concept of attribute reduction is of great significance in machine learning research. In previous studies, a variety of attribute reduction definitions have been proposed according to different rules. Based on the binary relations among objects and local decision rules, this paper describes a local indiscernibility relation reduction for information tables. The discernibility matrix for the proposed reduction is established, and examples for single-and multi-decision classes are presented to illustrate that the proposed local indiscernibility relation reduction can be applied to decision tables. According to the reduction concept developed in this paper, and considering a heuristic algorithm for calculating the significance of attributes and a binary integer programming algorithm based on the discernibility matrix, three reduction algorithms are proposed. Experiments are conducted using four classifiers and a number of publicly available datasets. A comparison of the experimental results presented in this paper demonstrates the feasibility of the proposed algorithms.

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Section: Local Indiscernibility Relation Reduction For Information Ta...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%