Abstract
Unsupervised classification is the choice when knowledge about the class numbers and the class properties is missing. However, using clustering might not lead to the correct class and needs interacting with the domain experts to figure out the classes that make sense for the respective domain. We propose to use a prototype-based learning and classification method in order to figure out the right number of classes and the class description. An expert might start with picking out a prototypical image or object for the class he is expecting. Later on, he might pick out some more prototypes that might represent the variance of the class. By doing so might be incrementally learnt the class border and the knowledge about the class. It does not need the expert so heavy interaction with the system. Such a method is especially useful when the domain has very noisy objects and images. We present in the paper the method for prototype-based classification, the methodology, and describe the success of the method on a biological application - the detection of different dynamic signatures of mitochondrial movement.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Index Terms
References
Schmidt, R., Gierl, L.: Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications. In: Perner, P. (ed.) MLDM 2001. LNCS (LNAI), vol. 2123, pp. 23–34. Springer, Heidelberg (2001)
Perner, P.: Prototype-Based Classification. Applied Intelligence 28, 238–246 (2008)
Nilsson, M., Funk, P.: A Case-Based Classification of Respiratory Sinus Arrhythmia. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 673–685. Springer, Heidelberg (2004)
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based Learning Algorithm. Machine Learning 6(1), 37–66 (1991)
Bichindaritz, I., Kansu, E., Sullivan, K.M.: Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 334–345. Springer, Heidelberg (1998)
Sachs-Hombach, K.: Bildbegriff und Bildwissenschaft. In: Gerhardus, D., Rompza, S. (eds.) Kunst - Gestaltung - Design, Heft 8, pp. 1–38. Verlag St. Johann, Saarbrücken (2002)
Krausz, E., Prechtl, S., Stelzer, E.H.K., Bork, P., Perner, P.: Quantitative Measurement of dynamic time dependent cellular events. Project Description (May 2006)
Chang, C.-L.: Finding Prototypes for Nearest Neighbor Classifiers. IEEE Trans. on Computers C-23(11) (1974)
Perner, P. (ed.): Data Mining on Multimedia Data. LNCS, vol. 2558. Springer, Heidelberg (2002)
Little, S., Colantonio, S., Salvetti, O., Perner, P.: Evaluation of Feature Subset Selection, Feature Weighting, and Prototype Selection for Biomedical Applications. J. Software Engineering & Applications 3, 39–49 (2010)
Niemeier, W.: Ausgleichsrechnung. de Gruyter, Berlin (2008)
Perner, P.: Novel Computerized Methods in System Biology–Flexible High-Content Image Analysis and Interpretation System for Cell Images. In: Perner, P., Salvetti, O. (eds.) MDA 2008. LNCS (LNAI), vol. 5108, pp. 139–157. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Perner, P. (2014). Detecting the Transition Stage of Cells and Cell Parts by Prototype-Based Classification. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science(), vol 8557. Springer, Cham. https://doi.org/10.1007/978-3-319-08976-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-08976-8_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08975-1
Online ISBN: 978-3-319-08976-8
eBook Packages: Computer ScienceComputer Science (R0)