Abstract
In this chapter we present a novel method for scoring function specification and feature selection by combining unsupervised learning with supervised cross validation. Various clustering algorithms such as one dimensional Kohonen SOM, k-means, fuzzy c-means and hierarchical clustering procedures are used to perform a clustering of object-data for a chosen subset of input features and a given number of clusters. The resulting object clusters are compared with the predefined target classes and a matching factor (score) is calculated. This score is used as criterion function for heuristic sequential and cross feature selection.
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References
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences 10(23) (1984)
Chen, Y., Abraham, A., Yang, B.: Feature selection and classification using flexible neural tree. Neurocomputing 70 (2006)
Campello, R.J.G.B.: A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Letters 28 (2007)
Faro, D., Maiorana, F.: Discovering complex regularities by adaptive Self Organizing classification. Proceedings of WASET 4 (2005)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On Clustering Validation Techniques. Journal of Intelligent Information Systems 17, 2–3 (2001)
Höppner, F.: Fuzzy cluster analysis: methods for classification, data analysis, and image recognition. Wiley Publ. (1999)
Jacak, W., Pröll, K.: Unsupervised Neural Networks based Scoring and Feature Selection in Biological Data Analysis. In: Proc. 14th International Asia Pacific Conference on Computer Aided System Theory, Sydney (2012)
Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall, Inc., Upper Saddle River (1988)
Jain, A.K., Zongker, D.: Feature Selection: Evaluation, Application and Small Sample Performance. IEEE Trans. PAMI 19(2) (1997)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2) (1997)
Kwak, N., Choi, C.: Input Feature Selection for Classification Problems. IEEE Transactions on Neural Networks 13(1) (2002)
Law, M.H.C., Figueiredo, M.A.T., Jain, A.K.: Simultaneous feature selection and clustering using mixture models. IEEE Trans. PAMI 26(9) (2004)
Legny, C., Juhsz, S., Babos, A.: Cluster validity measurement techniques. World Scientific and Engineering Academy and Society, WSEAS (2006)
Lee, K., Booth, D., State, K., Alam, P.: Backpropagation and Kohonen Self-Organizing Feature Map in Bankruptcy Prediction. In: Zhang, G.P. (ed.) Neural Networks in Business Forecasting. Idea Group Inc. (2004)
Maiorana, F.: Feature Selection with Kohonen Self Organizing Classification Algorithm, World Academy of Science, Engineering and Technology. Proceedings of WASET 45 (2008)
Su, M.-C., Chang, H.-T.: Fast Self-Organizing Feature Map Algorithm. IEEE Transactions on Neural Networks 11(3) (2000)
Su, M.-C., Liu, T.-K., Chang, H.-T.: Improving the Self-Organizing Feature Map Algorithm Using an Efficient Initialization Scheme. Journal of Science and Engineering 5(1) (2002)
Pal, S.K., De, R.K., Basak, J.: Unsupervised Feature Evaluation: A Neuro-Fuzzy Approach. IEEE Transactions on Neural Networks 11(2) (2000)
Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems (3) (1995)
Raghavendra, B.K., Simha, J.B.: Evaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring. Int. J. Advanced Networking and Applications 2(3) (2010)
Silva, B., Marques, N.: Feature clustering with self-organizing maps and an application to financial time-series for portfolio selection. In: Proceedings of Intern. Conf. of Neural Computation, ICNC (2010)
Thangavel, K., Shen, Q., Pethalakshmi, A.: Application of Clustering for Feature Selection Based on Rough Set Theory Approach. AIML Journal 6(1) (January 2006)
Törmä, M.: Self-organizing neural networks in feature extraction. Intern. Arch. of Photogrammetry and Remote Sensing XXXI, Part 2 (1996)
Winkler, S., Affenzeller, M., Kronberger, G., Kommenda, M., Wagner, S., Jacak, W., Stekel, H.: Feature selection in the analysis of tumor marker data using evolutionary algorithms. In: Proceedings of the 7th International Mediterranean and Latin American Modelling Multiconference, EMSS 2010 (2010)
Vendramin, L., Campello, R.J.G.B., Hruschka, E.R.: Relative clustering validity criteria: A comparative overview. Statistical Analysis and Data Mining 3(4) (2010)
Zhang, G.P.: Neural Networks for Classification: A Survey. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews 30(4) (2000)
Ye, H., Liu, H.: A SOM-based method for feature selection. In: Proceedings of the 9th International Conference on Neural Information Processing (ICONIP 2002), vol. 3 (2002)
Epstein, M. (ed.): Proc. of GMSAFOOD Conference, Vienna (2012)
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Jacak, W., Pröll, K., Winkler, S. (2014). Neural Networks Based Feature Selection in Biological Data Analysis. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol 6. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01436-4_5
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DOI: https://doi.org/10.1007/978-3-319-01436-4_5
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