·500· 智能系统学报 第16卷 度、决策性能以及计算效率3个方面与传统批量 based systems,2002,10(1):95-103 式特征选择算法进行了性能对比。实验结果表 [11]QIAN Yuhua,LIANG Jiye,WANG Feng.A new method 明，本文算法所选择的特征子集与批量式算法在 for measuring the uncertainty in incomplete information 分类精度和决策性能具有基本一致的性能表现。 systems[J].International journal of uncertainty,fuzziness 同时，在面对不完备数据中特征值的动态变化环 and knowledge-based systems,2009,17(6):855-880. 境下，本文算法的计算效率远优于传统批量式算 [12]DAI Jianhua,WANG Wentao,XU Qing.An uncertainty 法，可在较短时间内计算出一个可行的特征子 measure for incomplete decision tables and its applica- 集。实验中部分数据集使用算法IFS-CE-IDS需 tions[J].IEEE transactions on cybernetics,2013,43(4): 要进行特征转化，导致失去部分有效信息，降低 1277-1289 [13]ZHAO Hua,QIN Keyun.Mixed feature selection inin- 算法结果质量，未来将致力于寻求更有效处理混 complete decision table[J].Knowledge-based systems, 合数据的增量特征算法。 2014,57:181-190. 参考文献： [14]RAGHAVAN V,HAFEZ A.Dynamic data mining[Cl// Proceedings of the 13th International Conference on In- [1]KWAK N,CHOI C H.Input feature selection by mutual dustrial and Engineering Applications of Artificial Intelli- information based on Parzen window[J].IEEE transac- gence and Expert Systems.New Orleans,Louisiana, tions on pattern analysis and machine intelligence,2002, USA.2000 2412):1667-1671. [15]LI Tianrui,RUAN Da,GEERT W,et al.A rough sets [2]SLOWINSKI R.VANDERPOOTEN D.A generalized based characteristic relation approach for dynamic attrib- definition of rough approximations based on similarity[J]. ute generalization in data mining[J].Knowledge-based IEEE transactions on knowledge and data engineering, systems,2007,20(5)485-494. 2000,12(2):331-336. [16]XU Yitian,WANG Laisheng,ZHANG Ruiyan.A dynam- [3]KRYSZKIEWICZ M.Rough set approach to incomplete ic attribute reduction algorithm based on 0-1 integer pro- information systems[J].Information sciences,1998, gramming[J].Knowledge-based systems,2011,24(8): 112(1/2/3/4:39-49. 1341-1347. [4]PARTHALAIN N M.SHEN Qiang.Exploring the bound- [17]QIAN Jin,DANG Chuangyin,YUE Xiaodong,et al.At- ary region of tolerance rough sets for feature selection[J]. tribute reduction for sequential three-way decisions under Pattern recognition,2009,42(5):655-667. dynamic granulation[J].International journal of approx- [5]MENG Zuqiang,SHI Zhongzhi.Extended rough set-based imate reasoning,2017,85:196-216. attribute reduction in inconsistent incomplete decision sys- [18]YANG Yanyan,CHEN Degang,WANG Hui,et al.Incre- tems[J].Information sciences,2012,204:44-69 mental perspective for feature selection based on fuzzy [6]GRZYMALA-BUSSEJ W.CLARK P G.KUEHNHAUSEN M. rough sets[J].IEEE transactions on fuzzy systems,2018. Generalized probabilistic approximations of incomplete 26(31257-1273. data[J].International journal of approximate reasoning, [19]LANG Guangming,CAI Mingjie,FUJITA H,et al.Re- 2014,55(1):180-196. lated families-based attribute reduction of dynamic cover- [7]QIAN Yuhua,LIANG Jiye,PEDRYCZ W.et al.An effi- ing decision information systems[J].Knowledge-based cient accelerator for attribute reduction from incomplete systems,.2018,162:161-173 data in rough set framework[J].Pattern recognition,2011, [20]WEI Wei,WU Xiaoying,LIANG Jiye,et al.Discernibil- 44(8):1658-1670 ity matrix based incremental attribute reduction for dy- [8]DAI Jianhua.Rough set approach to incomplete numerical namic data[J].Knowledge-based systems,2018,140: data[J].Information sciences,2013,241:43-57. 142-157. [9]YANG Xibei,YANG Jingyu,WU Chen,et al.Dominance- [21]ZENG Anping,LI Tianrui,LIU Dun,et al.A fuzzy rough based rough set approach and knowledge reductions in in- set approach for incremental feature selection on hybrid complete ordered information system[.Information sci- information systems[J].Fuzzy sets and systems,2015, ences,2008,178(4):1219-1234 258:39-60 [10]LIANG Jiye,XU Zongben.The algorithm on knowledge [22]LIANG Jiye,WANG Feng,DANG Chuangyin,et al.A reduction in incomplete information systems[J].Interna- group incremental approach to feature selection applying tional journal of uncertainty,fuzziness and knowledge rough set technique[J].IEEE transactions on knowledge度、决策性能以及计算效率 3 个方面与传统批量 式特征选择算法进行了性能对比。实验结果表 明，本文算法所选择的特征子集与批量式算法在 分类精度和决策性能具有基本一致的性能表现。 同时，在面对不完备数据中特征值的动态变化环 境下，本文算法的计算效率远优于传统批量式算 法，可在较短时间内计算出一个可行的特征子 集。实验中部分数据集使用算法 IFS-CE-IDS 需 要进行特征转化，导致失去部分有效信息，降低 算法结果质量，未来将致力于寻求更有效处理混 合数据的增量特征算法。 参考文献： KWAK N, CHOI C H. Input feature selection by mutual information based on Parzen window[J]. IEEE transac￾tions on pattern analysis and machine intelligence, 2002, 24(12): 1667–1671. [1] SLOWINSKI R, VANDERPOOTEN D. A generalized definition of rough approximations based on similarity[J]. IEEE transactions on knowledge and data engineering, 2000, 12(2): 331–336. [2] KRYSZKIEWICZ M. Rough set approach to incomplete information systems[J]. Information sciences, 1998, 112(1/2/3/4): 39–49. [3] PARTHALÁIN N M, SHEN Qiang. Exploring the bound￾ary region of tolerance rough sets for feature selection[J]. Pattern recognition, 2009, 42(5): 655–667. [4] MENG Zuqiang, SHI Zhongzhi. Extended rough set-based attribute reduction in inconsistent incomplete decision sys￾tems[J]. Information sciences, 2012, 204: 44–69. [5] GRZYMALA-BUSSE J W, CLARK P G, KUEHNHAUSEN M. Generalized probabilistic approximations of incomplete data[J]. International journal of approximate reasoning, 2014, 55(1): 180–196. [6] QIAN Yuhua, LIANG Jiye, PEDRYCZ W, et al. An effi￾cient accelerator for attribute reduction from incomplete data in rough set framework[J]. Pattern recognition, 2011, 44(8): 1658–1670. [7] DAI Jianhua. Rough set approach to incomplete numerical data[J]. Information sciences, 2013, 241: 43–57. [8] YANG Xibei, YANG Jingyu, WU Chen, et al. Dominance￾based rough set approach and knowledge reductions in in￾complete ordered information system[J]. Information sci￾ences, 2008, 178(4): 1219–1234. [9] LIANG Jiye, XU Zongben. The algorithm on knowledge reduction in incomplete information systems[J]. Interna￾tional journal of uncertainty, fuzziness and knowledge- [10] based systems, 2002, 10(1): 95–103. QIAN Yuhua, LIANG Jiye, WANG Feng. A new method for measuring the uncertainty in incomplete information systems[J]. International journal of uncertainty, fuzziness and knowledge-based systems, 2009, 17(6): 855–880. [11] DAI Jianhua, WANG Wentao, XU Qing. An uncertainty measure for incomplete decision tables and its applica￾tions[J]. IEEE transactions on cybernetics, 2013, 43(4): 1277–1289. [12] ZHAO Hua, QIN Keyun. Mixed feature selection in in￾complete decision table[J]. Knowledge-based systems, 2014, 57: 181–190. [13] RAGHAVAN V, HAFEZ A. Dynamic data mining[C]// Proceedings of the 13th International Conference on In￾dustrial and Engineering Applications of Artificial Intelli￾gence and Expert Systems. New Orleans, Louisiana, USA, 2000. [14] LI Tianrui, RUAN Da, GEERT W, et al. A rough sets based characteristic relation approach for dynamic attrib￾ute generalization in data mining[J]. Knowledge-based systems, 2007, 20(5): 485–494. [15] XU Yitian, WANG Laisheng, ZHANG Ruiyan. A dynam￾ic attribute reduction algorithm based on 0-1 integer pro￾gramming[J]. Knowledge-based systems, 2011, 24(8): 1341–1347. [16] QIAN Jin, DANG Chuangyin, YUE Xiaodong, et al. At￾tribute reduction for sequential three-way decisions under dynamic granulation[J]. International journal of approx￾imate reasoning, 2017, 85: 196–216. [17] YANG Yanyan, CHEN Degang, WANG Hui, et al. Incre￾mental perspective for feature selection based on fuzzy rough sets[J]. IEEE transactions on fuzzy systems, 2018, 26(3): 1257–1273. [18] LANG Guangming, CAI Mingjie, FUJITA H, et al. Re￾lated families-based attribute reduction of dynamic cover￾ing decision information systems[J]. Knowledge-based systems, 2018, 162: 161–173. [19] WEI Wei, WU Xiaoying, LIANG Jiye, et al. Discernibil￾ity matrix based incremental attribute reduction for dy￾namic data[J]. Knowledge-based systems, 2018, 140: 142–157. [20] ZENG Anping, LI Tianrui, LIU Dun, et al. A fuzzy rough set approach for incremental feature selection on hybrid information systems[J]. Fuzzy sets and systems, 2015, 258: 39–60. [21] LIANG Jiye, WANG Feng, DANG Chuangyin, et al. A group incremental approach to feature selection applying rough set technique[J]. IEEE transactions on knowledge [22] ·500· 智 能 系 统 学 报 第 16 卷