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2018 | OriginalPaper | Buchkapitel

10. Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data

verfasst von : Annamária Szenkovits, Regina Meszlényi, Krisztian Buza, Noémi Gaskó, Rodica Ioana Lung, Mihai Suciu

Erschienen in: Advances in Feature Selection for Data and Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Recent advances in brain imaging technology, coupled with large-scale brain research projects, such as the BRAIN initiative in the U.S. and the European Human Brain Project, allow us to capture brain activity in unprecedented details. In principle, the observed data is expected to substantially shape our knowledge about brain activity, which includes the development of new biomarkers of brain disorders. However, due to the high dimensionality, the analysis of the data is challenging, and selection of relevant features is one of the most important analytic tasks. In many cases, due to the complexity of search space, evolutionary algorithms are appropriate to solve the aforementioned task. In this chapter, we consider the feature selection task from the point of view of classification tasks related to functional magnetic resonance imaging (fMRI) data. Furthermore, we present an empirical comparison of conventional LASSO-based feature selection and a novel feature selection approach designed for fMRI data based on a simple genetic algorithm.

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Vorheriges Kapitel Feature Selection Approach for Rule-Based Knowledge Bases

Nächstes Kapitel Shape Descriptions and Classes of Shapes. A Proximal Physical Geometry Approach

The accuracy for the fitness function of mGA was calculated solely on the training data. In particular we measured the accuracy of a nearest neighbor classifier in an internal 5-fold cross-validation on the training data.

We note that \(\lambda = 0.001\) and \(\lambda = 0.0001\) led to very similar classification accuracy. For simplicity, we only show the results in case of \(\lambda = 0.005\) in Sect. 10.3.

Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)

Bassett, D.S., Sporns, O.: Network neuroscience. Nat. Neurosci. 20(3), 353–364 (2017)CrossRef

Blautzik, J., Keeser, D., Berman, A., Paolini, M., Kirsch, V., Mueller, S., Coates, U., Reiser, M., Teipel, S.J., Meindl, T.: Long-term test-retest reliability of resting-state networks in healthy elderly subjects and patients with amnestic mild cognitive impairment. J. Alzheimer’s Dis. 34(3), 741–754 (2013)

Blautzik, J., Vetter, C., Peres, I., Gutyrchik, E., Keeser, D., Berman, A., Kirsch, V., Mueller, S., Pöppel, E., Reiser, M., et al.: Classifying fmri-derived resting-state connectivity patterns according to their daily rhythmicity. NeuroImage 71, 298–306 (2013)CrossRef

Buza, K., Nanopoulos, A., Schmidt-Thieme, L.: Time-series classification based on individualised error prediction. In: 13th International Conference on Computational Science and Engineering, pp. 48–54. IEEE (2010)

Canuto, A.M.P., Nascimento, D.S.C.: A genetic-based approach to features selection for ensembles using a hybrid and adaptive fitness function. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2012). https://doi.org/10.1109/IJCNN.2012.6252740

Chang, E.I., Lippmann, R.P.: Using genetic algorithms to improve pattern classification performance. In: Proceedings of the 1990 Conference on Advances in Neural Information Processing Systems 3. NIPS-3, pp. 797–803. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1990)

Chen, G.H., Nikolov, S., Shah, D.: A latent source model for nonparametric time series classification. In: Advances in Neural Information Processing Systems, pp. 1088–1096 (2013)

D’Alessandre, M., Vachtseyanos, G., Esteller, R., Echauz, J., Sewell, D., Litt, B.: A systematic approach to seizure prediction using genetic and classifier based feature selection. In: International Conference on Digital Signal Processing, DSP, vol. 2 (2002). https://doi.org/10.1109/ICDSP.2002.1028162

10.

De Jong, K.: Evolutionary Computation: A Unified Approach. MIT Press, Bradford Book (2006)MATH

11.

Devroye, L., Gyorfi, L., Krzyzak, A., Lugosi, G.: On the strong universal consistency of nearest neighbor regression function estimates. Ann. Stat. 1371–1385 (1994)

12.

Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing, 2nd edn. Springer Publishing Company, Incorporated (2015). https://doi.org/10.1007/978-3-662-44874-8

13.

de la Fraga, L.G., Coello Coello, C.A.: A review of applications of evolutionary algorithms in pattern recognition. In: Wang, P.S.P. (ed.) Pattern Recognition, Machine Intelligence and Biometrics, pp. 3–28. Springer Berlin, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22407-2_1

14.

Grundman, M., Petersen, R.C., Ferris, S.H., Thomas, R.G., Aisen, P.S., Bennett, D.A., Foster, N.L., Jack Jr., C.R., Galasko, D.R., Doody, R., et al.: Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. Arch. Neurol. 61(1), 59–66 (2004)

15.

Gwalani, H., Mittal, N., Vidyarthi, A.: Classification of brain tumours using genetic algorithms as a feature selection method (GAFS). In: ACM International Conference Proceeding Series, vol. 25–26, August (2016). https://doi.org/10.1145/2980258.2980318

16.

Herculano-Houzel, S.: The human brain in numbers: a linearly scaled-up primate brain. Front. Hum. Neurosci. 3, 31 (2009)CrossRef

17.

Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)CrossRefMATH

18.

Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, USA (1992)

19.

de la Hoz, E., de la Hoz, E., Ortiz, A., Ortega, J., Martínez-Álvarez, A.: Feature selection by multi-objective optimisation: application to network anomaly detection by hierarchical self-organising maps. Knowl. Based Syst. 71, 322–338 (2014). https://doi.org/10.1016/j.knosys.2014.08.013 CrossRef

20.

Hyvärinen, J., Carlson, S., Hyvärinen, L.: Early visual deprivation alters modality of neuronal responses in area 19 of monkey cortex. Neurosci. Lett. 26(3), 239–243 (1981)CrossRef

21.

de la Iglesia, B.: Evolutionary computation for feature selection in classification problems. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 3(6), 381–407 (2013). https://doi.org/10.1002/widm.1106 CrossRef

22.

Jalili, M.: Graph theoretical analysis of Alzheimer’s disease: discrimination of AD patients from healthy subjects. Inf. Sci. 384 (2017). https://doi.org/10.1016/j.ins.2016.08.047

23.

Ji, Y., Bu, X., Sun, J., Liu, Z.: An improved simulated annealing genetic algorithm of EEG feature selection in sleep stage. In: 2016, Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. APSIPA 2016 (2017). https://doi.org/10.1109/APSIPA.2016.7820683

24.

Kaya, Y., Pehlivan, H.: Feature selection using genetic algorithms for premature ventricular contraction classification. In: 2015 9th International Conference on Electrical and Electronics Engineering (ELECO), pp. 1229–1232 (2015). https://doi.org/10.1109/ELECO.2015.7394628

25.

Khan, A., Baig, A.: Multi-objective feature subset selection using non-dominated sorting genetic algorithm. J. Appl. Res. Technol. 13(1), 145–159 (2015). https://doi.org/10.1016/S1665-6423(15)30013-4 CrossRef

26.

Kharrat, A., Halima, M., Ben Ayed, M.: MRI brain tumor classification using Support Vector Machines and meta-heuristic method. In: International Conference on Intelligent Systems Design and Applications, ISDA, vol. 2016, June (2016). https://doi.org/10.1109/ISDA.2015.7489271

27.

Lichtman, J.W., Denk, W.: The big and the small: challenges of imaging the brain’s circuits. Science 334(6056), 618–623 (2011)CrossRef

28.

Meszlényi, R., Peska, L., Gál, V., Vidnyánszky, Z., Buza, K.: Classification of fmri data using dynamic time warping based functional connectivity analysis. In: Signal Processing Conference (EUSIPCO), 2016 24th European, pp. 245–249. IEEE (2016)

29.

Meszlényi, R., Peska, L., Gál, V., Vidnyánszky, Z., Buza, K.A.: A model for classification based on the functional connectivity pattern dynamics of the brain. In: Third European Network Intelligence Conference, pp. 203–208 (2016)

30.

Meszlényi, R.J., Hermann, P., Buza, K., Gál, V., Vidnyánszky, Z.: Resting state fmri functional connectivity analysis using dynamic time warping. Front. Neurosci. 11, 75 (2017)CrossRef

31.

Michalewicz, Z., Dasgupta, D. (eds.): Evolutionary Algorithms in Engineering Applications, 1st edn. Springer-Verlag New York Inc, Secaucus, NJ, USA (1997)MATH

32.

Noori, F., Qureshi, N., Khan, R., Naseer, N.: Feature selection based on modified genetic algorithm for optimization of functional near-infrared spectroscopy (fNIRS) signals for BCI. In: 2016 2nd International Conference on Robotics and Artificial Intelligence, ICRAI 2016 (2016). https://doi.org/10.1109/ICRAI.2016.7791227

33.

Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality reduction using genetic algorithms. IEEE Trans. Evol. Comput. 4(2), 164–171 (2000). https://doi.org/10.1109/4235.850656 CrossRef

34.

Richiardi, J., Altmann, A., Milazzo, A.C., Chang, C., Chakravarty, M.M., Banaschewski, T., Barker, G.J., Bokde, A.L., Bromberg, U., Büchel, C., et al.: Correlated gene expression supports synchronous activity in brain networks. Science 348(6240), 1241–1244 (2015)CrossRef

35.

Rosa, M.J., Portugal, L., Hahn, T., Fallgatter, A.J., Garrido, M.I., Shawe-Taylor, J., Mourao-Miranda, J.: Sparse network-based models for patient classification using fmri. Neuroimage 105, 493–506 (2015)CrossRef

36.

Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)CrossRefMATH

37.

Sanchez, E., Squillero, G., Tonda, A.: Industrial Applications of Evolutionary Algorithms. Springer-Verlag Berlin Heidelberg (2012). https://doi.org/10.1007/978-3-642-27467-1

38.

Schroeter, M.L., Stein, T., Maslowski, N., Neumann, J.: Neural correlates of Alzheimer’s disease and mild cognitive impairment: a systematic and quantitative meta-analysis involving 1351 patients. Neuroimage 47(4), 1196–1206 (2009)

39.

da Silva, S.F., Ribeiro, M.X., João do E.S. Batista Neto, J., Traina-Jr., C., Traina, A.J.: Improving the ranking quality of medical image retrieval using a genetic feature selection method. Decis. Support Syst. 51(4), 810 – 820 (2011). https://doi.org/10.1016/j.dss.2011.01.015. (Recent Advances in Data, Text, and Media Mining & Information Issues in Supply Chain and in Service System Design)

40.

Stańczyk, U.: On performance of DRSA-ANN classifier. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 172–179. Springer (2011)

41.

Tajik, M., Rehman, A., Khan, W., Khan, B.: Texture feature selection using GA for classification of human brain MRI scans. Lecture Notes in Computer Science, vol. 9713. Springer International Publishing, Switzerland (2016)

42.

Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.)267–288 (1996)

43.

White, J.G., Southgate, E., Thomson, J.N., Brenner, S.: The structure of the nervous system of the nematode caenorhabditis elegans. Philos. Trans. R. Soc. Lond. B Biol. Sci. 314(1165), 1–340 (1986)CrossRef

44.

Winkler, S.M., Affenzeller, M., Jacak, W., Stekel, H.: Identification of cancer diagnosis estimation models using evolutionary algorithms: a case study for breast cancer, melanoma, and cancer in the respiratory system. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO’11, pp. 503–510. ACM, New York, NY, USA (2011). https://doi.org/10.1145/2001858.2002040

45.

Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016). https://doi.org/10.1109/TEVC.2015.2504420 CrossRef

46.

Yaka, R., Yinon, U., Rosner, M., Wollberg, Z.: Pathological and experimentally induced blindness induces auditory activity in the cat primary visual cortex. Exp. Brain Res. 131(1), 144–148 (2000)CrossRef

47.

Yang, J., Honavar, V.G.: Feature subset selection using a genetic algorithm. IEEE Intell. Syst. 13(2), 44–49 (1998). https://doi.org/10.1109/5254.671091 CrossRef

48.

Yang, J., Pan, P., Song, W., Huang, R., Li, J., Chen, K., Gong, Q., Zhong, J., Shi, H., Shang, H.: Voxelwise meta-analysis of gray matter anomalies in Alzheimer’s disease and mild cognitive impairment using anatomic likelihood estimation. J. Neurol. Sci. 316(1), 21–29 (2012)

49.

Ye, C.Q., Poo, M.M., Dan, Y., Zhang, X.H.: Synaptic mechanisms of direction selectivity in primary auditory cortex. J. Neurosci. 30(5), 1861–1868 (2010)

50.

Yoshor, D., Bosking, W.H., Ghose, G.M., Maunsell, J.H.: Receptive fields in human visual cortex mapped with surface electrodes. Cereb. Cortex 17(10), 2293–2302 (2007)CrossRef

51.

Zielosko, B., Chikalov, I., Moshkov, M., Amin, T.: Optimization of decision rules based on dynamic programming approach. In: Faucher, C., Jain, L.C. (eds.) Innovations in Intelligent Machines-4: Recent Advances in Knowledge Engineering, pp. 369–392. Springer (2014)

52.

Zuo, X.N., Anderson, J.S., Bellec, P., Birn, R.M., Biswal, B.B., Blautzik, J., Breitner, J.C., Buckner, R.L., Calhoun, V.D., Castellanos, F.X., et al.: An open science resource for establishing reliability and reproducibility in functional connectomics. Sci. Data 1 (2014)

Titel: Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data
verfasst von: Annamária Szenkovits
Regina Meszlényi
Krisztian Buza
Noémi Gaskó
Rodica Ioana Lung
Mihai Suciu
Verlag: Springer International Publishing
Buch: Advances in Feature Selection for Data and Pattern Recognition
Print ISBN: 978-3-319-67587-9

Electronic ISBN: 978-3-319-67588-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-67588-6_10

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