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Attribute Reduction Based on Lift and Random Sampling. Symmetry (Basel) 2022. [DOI: 10.3390/sym14091828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
As one of the key topics in the development of neighborhood rough set, attribute reduction has attracted extensive attentions because of its practicability and interpretability for dimension reduction or feature selection. Although the random sampling strategy has been introduced in attribute reduction to avoid overfitting, uncontrollable sampling may still affect the efficiency of search reduct. By utilizing inherent characteristics of each label, Multi-label learning with Label specIfic FeaTures (Lift) algorithm can improve the performance of mathematical modeling. Therefore, here, it is attempted to use Lift algorithm to guide the sampling for reduce the uncontrollability of sampling. In this paper, an attribute reduction algorithm based on Lift and random sampling called ARLRS is proposed, which aims to improve the efficiency of searching reduct. Firstly, Lift algorithm is used to choose the samples from the dataset as the members of the first group, then the reduct of the first group is calculated. Secondly, random sampling strategy is used to divide the rest of samples into groups which have symmetry structure. Finally, the reducts are calculated group-by-group, which is guided by the maintenance of the reducts’ classification performance. Comparing with other 5 attribute reduction strategies based on rough set theory over 17 University of California Irvine (UCI) datasets, experimental results show that: (1) ARLRS algorithm can significantly reduce the time consumption of searching reduct; (2) the reduct derived from ARLRS algorithm can provide satisfying performance in classification tasks.
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Zhang W, Kumar M, Ding W, Li X, Yu J. Variational learning of deep fuzzy theoretic nonparametric model. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ding W, Qin T, Shen X, Ju H, Wang H, Huang J, Li M. Parallel incremental efficient attribute reduction algorithm based on attribute tree. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.08.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Pishgoo B, Azirani AA, Raahemi B. A dynamic feature selection and intelligent model serving for hybrid batch-stream processing. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Yang X, Chen H, Li T, Luo C. A noise-aware fuzzy rough set approach for feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Sowkuntla P, Prasad PSVSS. MapReduce based parallel fuzzy-rough attribute reduction using discernibility matrix. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02253-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Guo Q, Qian Y, Liang X. GLRM: Logical pattern mining in the case of inconsistent data distribution based on multigranulation strategy. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Wei Q, Wang C, Wen Y. Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR.
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Affiliation(s)
- Qianjin Wei
- Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China
| | - Chengxian Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Yimin Wen
- Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China
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