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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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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