Feature selection techniques with class separability for multivariate time series

Neurocomputing

Volumn 110, Issue , 2013, Pages 29-34

  Han, Min ^a   Liu, Xiaoxin ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Class separability; Feature selection; Multivariate time series; Mutual information

Indexed keywords

CLASS SEPARABILITY; CLASS-SEPARABILITY CRITERION; EEG DATUM; FEA-TURE SELECTIONS; FEATURE SELECTION ALGORITHM; FILTER METHOD; MULTIVARIATE TIME SERIES; MUTUAL INFORMATIONS;

TIME SERIES;

FEATURE EXTRACTION;

ACROSS GROUP VARIANCE; ALCOHOLISM; ANALYTIC METHOD; ARTICLE; CLASS SEPARABILITY FEATURE SELECTION; CLEVER; CONTROLLED STUDY; CORRELATION AS FEATURES; CORRELATION COEFFICIENT; DATA MINING; ELECTROENCEPHALOGRAPHY; ENTROPY; HUMAN; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; MULTIVARIATE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; SUPPORT VECTOR MACHINE;

EID: 84876143470     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.12.006     Document Type: Article

References (38)

1. Javed K., Babri H., Saeed M. Feature selection based on class-dependent densities for high-dimensional binary data. IEEE Trans. Knowl. Data Eng. 2012, 24:465-477.
2. Monirul Kabir M., Monirul Islam M., Murase K. A new wrapper feature selection approach using neural network. Neurocomputing 2010, 73:3273-3283.
3. Kabir M.M., Shahjahan M., Murase K. A new local search based hybrid genetic algorithm for feature selection. Neurocomputing 2011, 74:2914-2928.
4. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
5. Martínez Sotoca J., Pla F. Supervised feature selection by clustering using conditional mutual information-based distances. Patt. Recog. 2010, 43:2068-2081.
6. Hong Y., Kwong S., Chang Y., Ren Q. Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm. Patt. Recog. 2008, 41:2742-2756.
7. Ferreira A.J., Figueiredo M.A.T. An unsupervised approach to feature discretization and selection. Patt. Recog. 2012, 45:3048-3060.
8. S. Cheema, T. Henne, U. Koeckemann, E. Prassler, Applicability of feature selection on multivariate time series data for robotic discovery, in: 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE '10) 2010, pp. V2-592-V592-597.
9. Zhang H., Liu J., Ma D., Wang Z. Data-core-based fuzzy min-max neural network for pattern classification. IEEE Trans. Neural Networks 2011, 22:2339-2352.
10. Fu T. A review on time series data mining. Eng. Appl. Artif. Intell. 2011, 24:164-181.
11. Ristanoski G., Bailey J. Distribution based data filtering for financial time series forecasting. AI 2011: Adv. Artif. Intell. 2011, 122-131.
12. Kadous M.W. Temporal classification: extending the classification paradigm to multivariate time series, Doctoral Dissertation, School of Computer Science and Engineering 2002, The University of New South Wales.
13. C. Shahabi, AIMS: an immersidata management system, in: VLDB First Biennial Conference on Innovative Data Systems Research (CIDR '03), 2003.
14. R. Tanawongsuwan, A. Bobick, Performance analysis of time-distance gait parameters under different speeds, in: 4th International Conference on Audio- and Video-based Biometric Person Authentication (AVBPA '03) Springer, 2003, pp. 715-724.
15. Zhang X.L., Begleiter H., Porjesz B., Wang W., Litke A. Event related potentials during object recognition tasks. Brain Res. Bull. 1995, 38:531-538.
16. Rémillard B., Papageorgiou N., Soustra F. Copula-based semiparametric models for multivariate time series. J. Multivariate Anal. 2012, 110:30-42.
17. Guyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn. 2002, 46:389-422.
18. Weston J., Elisseeff A., Schölkopf B., Tipping M. Use of the zero norm with linear models and kernel methods. J. Mach. Learn. Res. 2003, 3:1439-1461.
19. Wöllmer M., Al-Hames M., Eyben F., Schuller B., Rigoll G. A multidimensional dynamic time warping algorithm for efficient multimodal fusion of asynchronous data streams. Neurocomputing 2009, 73:366-380.
20. Hoeppner F. Discovery of temporal patterns. Prin. Data Min. Knowl. Discovery 2001, 192-203.
21. Zhao Y., Zhang S. Generalized dimension-reduction framework for recent-biased time series analysis. IEEE Trans. Knowl. Data Eng. 2006, 18:231-244.
22. Chaovalit P., Gangopadhyay A., Karabatis G., Chen Z. Discrete wavelet transform-based time series analysis and mining. ACM Comput. Surv. (CSUR) 2011, 43:6.
23. Lal T.N., Schroder M., Hinterberger T., Weston J., Bogdan M., Birbaumer N., Scholkopf B. Support vector channel selection in BCI. IEEE Trans. Biomed. Eng. 2004, 51:1003-1010.
24. Wu D., Ke Y., Yu J., Yu P., Chen L. Detecting Leaders From Correlated Time Series, in: DASFAA 2010, Springer, pp. 352-367.
25. Moon T.K., Stirling W.C. Mathematical methods and algorithms for signal processing 2000, Prentice Hall.
26. Yoon H., Yang K., Shahabi C. Feature subset selection and feature ranking for multivariate time series. IEEE Trans. Knowl. Data Eng. 2005, 17:1186-1198.
27. Krzanowski W. Between-groups comparison of principal components. J. Am. Stat. Assoc. 1979, 74:703-707.
28. K. Yang, H. Yoon, C. Shahabi, A supervised feature subset selection technique for multivariate time series, in: Workshop Feature Selection for Data Mining: Interfacing Machine Learning with Statistics, 2005, pp. 92-101.
29. Dias N.S., Kamrunnahar M., Mendes P., Schiff S., Correia J. Feature selection on movement imagery discrimination and attention detection. Med. Biol. Eng. Comput. 2010, 48:331-341.
30. N. Dias, L. Jacinto, P. Mendes, J. Correia, Feature down-selection in Brain-Computer Interfaces, in: 4th International IEEE EMBS Conference on Neural Engineering IEEE, 2009, pp. 323-326.
31. Doquire G., Verleysen M. Feature selection with missing data using mutual information estimators. Neurocomputing 2012, 90:3-11.
32. Shannon C.E. A mathematical theory of communication. ACM SIGMOBILE Mobile Comput. Commun. Rev. 2001, 5:3-55.
33. Wang L. Feature selection with kernel class separability. IEEE Trans. Patt. Anal. Mach. Intell. 2008, 30:1534-1546.
34. Chang C.C., Lin C.J. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Tech. (TIST) 2011, 2:27.
35. Brown G., Pocock A., Zhao M.J., Luján M. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J. Mach. Learn. Res. 2012, 13:26-66.
36. C.L. Blake, C.J. Merz, UCI Repository of Machine Learning Databases. Irvine, CA: University of California, http://www.ics.uci.edu/(mlearn/MLRepository.html.
37. A. Yazdani, S. Kamaledin Setarehdan, Classification of EEG signals correlated with alcohol abusers, in: 9th International Symposium Signal Processing and its Applications (ISSPA '07), 2007, pp. 1-4.
38. Mitra P., Murthy C., Pal S.K. Unsupervised feature selection using feature similarity. IEEE Trans. Patt. Anal. Mach. Intell. 2002, 24:301-312.