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Ensemble Universum SVM Learning for Multimodal Classification of Alzheimer’s Disease

  • Xiaoke Hao
  • Daoqiang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

Abstract

Recently, machine learning methods (e.g., support vector machine (SVM)) have received increasing attentions in neuroimaging-based Alzheimer’s disease (AD) classification studies. For classifying AD patients from normal controls (NC), standard SVM trains a classification model from only AD and NC subjects. However, in practice besides AD and NC subjects, there may also exist other subjects such as those with mild cognitive impairment (MCI). In this paper, we investigate the potential of using MCI subjects to aid the identification of AD from NC subjects. Specifically, we propose to use the universum support vector machine (U-SVM) learning by treating MCI subjects as the universum examples that do not belong to either of the classes (i.e., AD and NC) of interest. The idea of U-SVM learning is to separate AD from NC subjects through large margin hyperplane with the universum MCI subjects laying inside the margin borders, which is in accordance with our domain knowledge that MCI is a prodromal stage of AD with cognitive status between NC and AD. Furthermore, we propose ensemble universum SVM learning for multimodal classification by training an individual U-SVM classifier for each modality. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate the efficacy of our proposed method.

Keywords

Mild Cognitive Impairment Normal Control Subject Mild Cognitive Impairment Subject Prodromal Stage Standard Support Vector Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867 (2011)CrossRefGoogle Scholar
  2. 2.
    Davatzikos, C., Bhatt, P., Shaw, L., Batmanghelich, K., Trojanowski, J.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol. Aging 32, e2322.e19–e2322.e27 (2011)Google Scholar
  3. 3.
    Cho, Y., Seong, J., Jeong, Y., Shin, S.: Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 59, 2217–2230 (2012)CrossRefGoogle Scholar
  4. 4.
    Zhang, D., Shen, D.: Semi-supervised multimodal classification of Alzheimer’s Disease. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1628–1631 (2011)Google Scholar
  5. 5.
    Cheng, B., Zhang, D., Shen, D.: Domain Transfer Learning for MCI Conversion Prediction. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 82–90. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Weston, J., Collobert, R., Sinz, F., Bottou, L., Vapnik, V.: Inference with the Universum. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 1009–1016 (2006)Google Scholar
  7. 7.
    Cherkassky, V., Dhar, S., Dai, W.: Practical conditions for effectiveness of the Universum learning. IEEE Trans. Neural Networks 22, 1241–1255 (2011)CrossRefGoogle Scholar
  8. 8.
    Westman, E., Muehlboeck, J., Simmons, A.: Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. Neuroimage 62, 229–238 (2012)CrossRefGoogle Scholar
  9. 9.
    Walhovd, K., Fjell, A., Brewer, J., McEvoy, L., Fennema-Notestine, C., Hagler, D., Jennings, R., Karow, D., Dale, A.: Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease. Am. J. Neuroradiol. 31, 347–354 (2010)CrossRefGoogle Scholar
  10. 10.
    Vapnik, V.: Estimation of dependences based on empirical data. Springer, New York (2006)zbMATHGoogle Scholar
  11. 11.
    Sinz, F., Chapelle, O., Agarwal, A., Scholkopf, B.: An Analysis of Inference with the Universum. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems (NIPS), pp. 1–8 (2008)Google Scholar
  12. 12.
    Tan, A., Gilbert, D.: Ensemble machine learning on gene expression data for cancer classification. Appl. Bioinformatics 2, S75–S83 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Xiaoke Hao
    • 1
  • Daoqiang Zhang
    • 1
  1. 1.Dept. of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

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