1 |
曾子林,张宏军,张睿,等.基于元学习思想的算法选择问题综述[J].控制与决策, 2014, 29(6): 961-968. 10.13195/j.kzyjc.2013.1297
|
|
ZENG Z L, ZHANG H J, ZHANG R, et al. Summary of algorithm selection problem based on meta-learning[J]. Control and Decision, 2014, 29(6): 961-968. 10.13195/j.kzyjc.2013.1297
|
2 |
AHA D W. Generalizing from case studies: a case study[M]// Machine Learning Proceedings 1992. San Francisco: Morgan Kaufmann, 1992: 1-10. 10.1016/b978-1-55860-247-2.50006-1
|
3 |
TATTI N. Distances between data sets based on summary statistics[J]. Journal of Machine Learning Research, 2007, 8: 131-154.
|
4 |
GNANADESIKAN R. Methods for Statistical Data Analysis of Multivariate Observations[M]. 2nd ed. New York: Wiley & Sons, Inc., 1997: 139-220. 10.1002/9781118032671
|
5 |
HO T K, BASU M. Complexity measures of supervised classification problems[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(3): 289-300. 10.1109/34.990132
|
6 |
BASU M, HO T K. Data Complexity in Pattern Recognition[M]. London: Springer, 2006: 48-66.
|
7 |
MACIÀ N, BERNADÓ-MANSILLA E, ORRIOLS-PUIG A, et al. Learner excellence biased by data set selection: a case for data characterisation and artificial data sets[J]. Pattern Recognition, 2013, 46(3): 1054-1066. 10.1016/j.patcog.2012.09.022
|
8 |
BERNHARD P, HILAN B. Meta-learning by landmarking various learning algorithms [C]// Proceedings of the 17th International Conference on Machine Learning. San Francisco: Morgan Kaufmann, 2000: 743-750.
|
9 |
WU X D, KUMAR V, QUINLAN J R, et al. Top 10 algorithms in data mining[J]. Knowledge and Information Systems, 2008, 14(1): 1-37. 10.1007/s10115-007-0114-2
|
10 |
SUN M X, LIU K H, WU Q Q, et al. A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis[J]. Pattern Recognition, 2019, 90: 346-362. 10.1016/j.patcog.2019.01.047
|
11 |
VILALTA R, DRISSI Y. A perspective view and survey of meta-learning[J]. Artificial Intelligence Review, 2002, 18(2): 77-95. 10.1023/a:1019956318069
|
12 |
GIRAUD-CARRIER C, VILALTA R, BRAZDIL P. Introduction to the special issue on meta-learning[J] Machine Learning, 2004, 54(3): 187-193. 10.1023/b:mach.0000015878.60765.42
|
13 |
BRAZDIL P, GIRAUD-CARRIER C. Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue[J] Machine Learning, 2018, 107(1): 1-14. 10.1007/s10994-017-5692-y
|
14 |
SMITH M R, MARTINEZ T, GIRAUD-CARRIER C. An instance level analysis of data complexity[J]. Machine Learning, 2014, 95(2): 225-256. 10.1007/s10994-013-5422-z
|
15 |
HO T K. A data complexity analysis of comparative advantages of decision forest constructors[J]. Pattern Analysis and Applications, 2002, 5(2): 102-112. 10.1007/s100440200009
|
16 |
BRODLEY C E. Recursive automatic bias selection for classifier construction[J]. Machine Learning, 1995, 20(1/2): 63-94. 10.1007/bf00993475
|
17 |
SCHAFFER C. Technical Note: selecting a classification method by cross-validation[J]. Machine Learning, 1993, 13(1): 135-143. 10.1007/bf00993106
|
18 |
GARCÍA S, LUENGO J, HERRERA F. Tutorial on practical tips of the most influential data preprocessing algorithms in data mining[J]. Knowledge-Based Systems, 2016, 98: 1-29. 10.1016/j.knosys.2015.12.006
|
19 |
XU X Z, LIANG T M, ZHU J, et al. Review of classical dimensionality reduction and sample selection methods for large-scale data processing[J]. Neurocomputing, 2019, 328: 5-15. 10.1016/j.neucom.2018.02.100
|
20 |
WANG X Z, XING H J, LI Y, et al. A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(5): 1638-1654. 10.1109/tfuzz.2014.2371479
|
21 |
SÁEZ J A, LUENGO J, HERRERA F. Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification[J]. Pattern Recognition, 2013, 46(1): 355-364. 10.1016/j.patcog.2012.07.009
|
22 |
LUENGO J, FERNÁNDEZ A, GARCÍA S, et al. Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling[J]. Soft Computing, 2011, 15(10): 1909-1936. 10.1007/s00500-010-0625-8
|
23 |
SÁNCHEZ J S, MOLLINEDA R A, SOTOCA J M. An analysis of how training data complexity affects the nearest neighbor classifiers[J]. Pattern Analysis and Applications, 2007, 10(3): 189-201. 10.1007/s10044-007-0061-2
|
24 |
CANO J R. Analysis of data complexity measures for classification[J]. Expert Systems with Applications, 2013, 40(12): 4820-4831. 10.1016/j.eswa.2013.02.025
|
25 |
BRUN A L, BRITTO A S, OLIVEIRA L S, et al. Contribution of data complexity features on dynamic classifier selection [C]// Proceedings of the 2016 International Joint Conference on Neural Networks. Piscataway: IEEE, 2016: 4396-4403. 10.1109/ijcnn.2016.7727774
|
26 |
LIU B D. Uncertainty Theory (Studies in Fuzziness and Soft Computing)[M]. 2nd ed. Berlin: Springer, 2007: 205-234.
|
27 |
LAI H L, ZHANG D X. Fuzzy preorder and fuzzy topology[J]. Fuzzy Sets and Systems, 2006, 157(14): 1865-1885. 10.1016/j.fss.2006.02.013
|
28 |
PAL M. Random forest classifier for remote sensing classification[J]. International Journal of Remote Sensing, 2005, 26(1): 217-222. 10.1080/01431160412331269698
|
29 |
WANG X Z, WANG R, XU C. Discovering the relationship between generalization and uncertainty by incorporating complexity of classification[J]. IEEE Transactions on Cybernetics, 2017, 48(2): 703-715. 10.1109/tcyb.2017.2653223
|
30 |
SHARMA A, SINGH S K. Early classification of time series based on uncertainty measure [C]// Proceedings of the 2019 IEEE Conference on Information and Communication Technology. Piscataway: IEEE, 2019: 1-6. 10.1109/cict48419.2019.9066213
|
31 |
SUN L, ZHANG X, QIAN Y, et al. Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification[J]. Information Sciences, 2019, 502: 18-41. 10.1016/j.ins.2019.05.072
|
32 |
XIAO F. A distance measure for intuitionistic fuzzy sets and its application to pattern classification problems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 51(6): 3980-3992. 10.1109/TSMC.2019.2958635
|
33 |
DE WAAL A, STEYN C. Uncertainty measurements in neural network predictions for classification tasks [C]// Proceedings of the IEEE 23rd International Conference on Information Fusion. Piscataway: IEEE, 2020: 1-7. 10.23919/fusion45008.2020.9190221
|