Abstract
Hierarchical classifiers are usually defined as methods of classifying inputs into defined output categories. The classification occurs first on a low-level with highly specific pieces of input data. The classifications of the individual pieces of data are then combined systematically and classified on a higher level iteratively until one output is produced. This final output is the overall classification of the data. In this paper we follow a controlled devise type of approach. The initial group of classifiers is trained using all objects in an information system S partitioned by values of the decision attribute d at its all granularity levels (one classifier per level). Only values of the highest granularity level (corresponding granules are the largest) are used to split S into information sub-systems where each one is built by selecting objects in S of the same decision value. These sub-systems are used for training new classifiers at all granularity levels of its decision attribute. Next, we split each sub-system further by sub-values of its decision value. The obtained tree-structure with groups of classifiers assigned to each of its nodes is called a cascade classifier. Given an incomplete information system with a hierarchical decision attribute d, we consider the problem of training classifiers describing values of d at its lowest granularity level. Taking MIRAI database of music instrument sounds [16], as an example, we show that the confidence of such classifiers can be lower than the confidence of cascade classifiers.
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References
Bruzzone, L., Cossu, R.: A Multiple Cascade-Classifier System for a Robust and Partially Unsupervised Updating of Land-Cover Maps, Technical Report DIT-02-026, Informatica e Telecomunicazioni, University of Trento, Italy (2002)
Dardzińska, A., Raś, Z.W.: Rule-Based Chase Algorithm for Partially Incomplete Information Systems. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 255–267. Springer, Heidelberg (2005)
Dardzińska, A., Raś, Z.W.: Chasing Unknown Values in Incomplete Information Systems. In: Lin, T.Y., Hu, X., Ohsuga, S., Liau, C. (eds.) Proceedings of ICDM 2003 Workshop on Foundations and New Directions of Data Mining, Melbourne, Florida, pp. 24–30. IEEE Computer Society, Los Alamitos (2003)
Duong, J., Emptoz, H.: Cascade Classifier: Design and Application to Digit Recognition. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 1065–1069. IEEE Computer Society, Los Alamitos (2005)
Haskell, R.-E.: Design of hierarchical classifiers. In: Sherwani, N.A., Kapenga, J.A., de Doncker, E. (eds.) Great Lakes CS Conference 1989. LNCS, vol. 507, pp. 118–124. Springer, Heidelberg (1989)
Huang, X., Li, S.Z., Wang, Y.: Learning with cascade for classification of non-convex manifolds. In: Proc. of CVPR Workshop on Face Processing in Video, FPIV 2004, Washington, DC (2004)
Im, S.: Privacy Aware Data Management and Chase. Fundamenta Informaticae 78(4), 507–524 (2007)
Kostek, B., Czyzewski, A.: Representing Musical Instrument Sounds for Their Automatic Classification. J. Audio Eng. Soc. 49(9), 768–785 (2001)
Kostek, B., Wieczorkowska, A.: Parametric Representation of Musical Sounds. Archive of Acoustics 22(1), 3–26 (1997)
Levene, M., Loizou, G.: Semantics for null extended nested relations. ACM Transactions on Database Systems (TODS) 18(3), 414–459 (1993)
Lu, C., Drew, M.S.: Construction of a hierarchical classifier schema using a combination of text-based and image-based approaches. In: SIGIR 2001 Proceedings, pp. 331–336. ACM Publications, New York (2001)
Martin, K.D., Kim, Y.E.: Musical instrument identification: a pattern-recognition approach. In: Proceedings of 136th Meeting of the Acoustical Society of America, Norfolk, VA (October 1998)
Michalski, R.S.: Attributional Ruletrees: A New Representation for AQ Learning, Reports of the Machine Learning and Inference Laboratory, MLI 02-1, George Mason University, Fairfax, VA (2002)
Pawlak, Z.: Information systems - theoretical foundations. Information Systems Journal 6, 205–218 (1991)
Raś, Z.W., Dardzińska, A., Zhang, X.: Cooperative Answering of Queries based on Hierarchical Decision Attributes. CAMES Journal, Polish Academy of Sciences, Institute of Fundamental Technological Research 14(4), 729–736 (2007)
Raś, Z.W., Zhang, X., Lewis, R.: MIRAI: Multi-hierarchical, FS-tree based music information retrieval system. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 80–89. Springer, Heidelberg (2007)
Yang, C.: The MACSIS Acoustic Indexing Framework for Music Retrieval: An Experimental Study. In: Proceedings of ISMIR 2002, pp. 53–62 (2002)
Zhang, X., Raś, Z.W., Dardzińska, A.: Discriminant Feature Analysis for Music Timbre Recognition and Automatic Indexing. In: Raś, Z.W., Tsumoto, S., Zighed, D.A. (eds.) MCD 2007. LNCS (LNAI), vol. 4944, pp. 104–115. Springer, Heidelberg (2008)
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Raś, Z.W., Dardzińska, A., Jiang, W. (2010). Cascade Classifiers for Hierarchical Decision Systems. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning I. Studies in Computational Intelligence, vol 262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05177-7_12
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DOI: https://doi.org/10.1007/978-3-642-05177-7_12
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