Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (3): 92-97.doi: 10.11896/jsjkx.190500180

• Database & Big Data & Data Science • Previous Articles     Next Articles

Class-specific Distribution Preservation Reduction in Interval-valued Decision Systems

YANG Wen-jing,ZHANG Nan,TONG Xiang-rong,DU Zhen-bin   

  1. (Key Lab for Data Science and Intelligence Technology of Shandong Higher Education Institutes, Yantai University, Yantai, Shandong 264005, China)
    (School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, China)
  • Received:2019-05-31 Online:2020-03-15 Published:2020-03-30
  • About author:YANG Wen-jing,born in 1996,postgraduate.Her main research interests include rough set theory,data mining and machine learning. ZHANG Nan,born in 1979,Ph.D,lecturer,master supervisor.His main research interests include rough set theory,cognitive informatics and artificial intelligence.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61572418, 61572419, 61873117, 61403329) and Shandong Provincial Natural Science Foundation (ZR2018BA004, ZR2016FM42).

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Abstract: Attribute reduction is one of the important areas in rough set theory.A minimal set of attributes which preserves a certain classification ability in decision tables is solved through a process of attribute reduction,and the process is to remove the redundant feature attributes and select the useful feature subset.A distribution reduct can preserve the distribution of all decision classes in decision tables,but the reducts of all decision classes may not be necessary in the practice.To solve the above problems,this paper proposed the concept of class-specific distribution preservation reduction based on α-tolerance relations in interval-valued decision systems.Some theorems of class-specific distribution preservation reduction were proved and the relevant discerni-bility matrix of class-specific distribution preservation reduction was constructed.And then this paper proposed class-specific distribution preservation reduction algorithm based on discernibility matrices (CDRDM),and analyzed the relationship between the set of non-empty elements in the discernibility matrices constructed by class-specific distribution preservation reduction algorithm and distribution preservation reduction algorithm (DRDM).In the experiment,six sets of UCI data sets were selected and the interval parameter was introduced.When the interval parameter is 1.2 and threshold is 0.5,the results and average length of reducts in DRDM algorithm and CDRDM algorithm were compared.When the interval parameter is 1.2 and 1.6 and threshold is 0.4 and 0.5 respectively,the changes of reduction time of DRDM algorithm and CDRDM algorithm with the number of objects and attributes were given.Moreover,the experiment indicates that CDRDM algorithm has different results for different decision classes.And when there are more than one decision class in decision tables,the average length of reducts of CDRDM algorithm is less than or equal to the average length of reducts of DRDM algorithm,the reduction efficiency based on different decision classes in CDRDM algorithm is improved in varying degrees.

Key words: Class-specific attribute reduction, Discernibility matrix, Distribution reduction, Interval-valued decision system, Rough set

CLC Number: 

  • TP181
[1]PAWLAK Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11(5):341-356.
[2]PAWLAK Z.Rough sets:Theoretical aspects of reasoning about data[M].Boston:Kluwer Academic Publishers,1992.
[3]MIAO D Q,HU G R.A Heuristic algorithm for reduction of knowledge[J].Journal of Computer Research and Development,1999,36(6):681- 684.
[4]QIAN Y H,LIANG J Y,PEDRYCZ W,et al.Positive approximation:an accelerator for attribute reduction in rough set theory[J].Artificial Intelligence,2010,174(9):597-618.
[5]QIAN Y H,LIANG X Y,WANG Q,et al.Local rough set:a solution to rough data analysis in big data[J].International Journal of Approximate Reasoning,2018,97:38-63.
[6]HU Q H,ZHANG L J,ZHOU Y C,et al.Large-Scale Multimodality Attribute Reduction With Multi-Kernel Fuzzy Rough Sets[J].IEEE Transactions on Fuzzy Systems,2018,26(1):226-238.
[7]JING Y G,LI T R,FUJITA H,et al.An incremental attribute reduction method for dynamic data mining[J].Information Sciences,2018,465:202-218.
[8]SKOWRON A,RAUSZER C.The discernibility matrices and functions in information systems[M]∥SLOWIHSKI R.Intelligent Decision Support.Dordrecht:Springer,1992:331-362.
[9]XU W H,ZHANG X Y,ZHONG J M,et al.Heuristic Algo- rithm for Attributes Reduction in Ordered Information Systems[J].Computer Engineering,2010,36(17):69-71.
[10]LEUNG Y,FISCHER M,WU W Z,et al.A rough set approach for the discovery of classification rules in interval-valued information systems[J].International Journal of Approximate Reasoning,2008,47(2):233-246.
[11]YANG X B,QI Y,YU D J,et al.α-Dominance relation and rough sets in interval-valued information systems[J].Information Sciences,2015,294(5):334-347.
[12]CHEN Z C,QIN K Y.Attribute Reduction of Interval-valued Information System Based on Variable Precision Relation[J].Computer science,2009,36(3):163-166.
[13]ZHANG N,MIAO D Q,YUE X D.Approaches to knowledge reduction in interval-valued information systems[J].Journal of Computer Research and Development,2010,47(8):1362-1371.
[14]SUN B Z,MA W M,GONG Z T.Dominance-based rough set theory over interval-valued information systems[M].John Wiley &Sons,2014.
[15]DAI J H,HU H,ZHENG G J,et al.Attribute reduction in interval-valued information systems based on information entropies[J].Frontiers of Information Technology and Electronic Engineering,2016,17(9):919-928.
[16]PINEDA-BAUTISTA B B,CARRASCO-OCHOA J A,MAR- TÍNEZ-TRINIDAD J F.General framework for class-specific feature selection[J].Expert Systems with Applications,2011,38(8):10018-10024.
[17]YAO Y Y,ZHANG X Y.Class-specific attribute reducts in rough set theory[J].Information Sciences,2017,418(38):601-618.
[18]LIU G L,HUA Z,ZOU J Y.Local attribute reductions for decision tables[J].Information Sciences,2017,422:204-217.
[19]YIN J L,ZHANG N,ZHAO L W,et al.Local Attribute Reduction in Interval-valued Decision Systems[J].Computer Science,2018,45(7):178-185.
[20]ZHANG N,XU X,TONG X R,et al.Distribution reduction in inconsistent interval-valued decision systems[J].Computer Science,2017,44(9):78-82,104.
[21]ZHANG X,MEI C L,CHEN D G,et al.Multi-confidence rule acquisition and confidence-preserved attribute reduction in interval-valued decision systems[J].International Journal of Approxi-mate Reasoning,2014,55(8):1787-1804.
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