Abstract
This paper presents a methodology of constructing robust classifiers based on a concept called a Hierarchic Similarity Model (HSM). The hierarchic similarity is interpreted as a relation between pairs of complex objects. This relation can be derived from an information system by examining the domain related aspects of similarity. In the paper, global similarity is decomposed into many local similarities by analogy with the process of perceiving similar objects. For the purpose of estimating local relations some well-known rough sets methods are used, as well as context knowledge provided by a domain expert. Then the rules modeling interactions between local similarities are constructed and used to assess the degree of a global similarity of complex objects. The obtained relation can be used to construct classifiers which may successfully compete with other popular methods like boosted decision trees or k-NN algorithm. An implementation of the proposed models in the R script language is provided together with an empirical evaluation of the similarity based classification accuracy for some common datasets. This paper is a continuation of the research started in [1].
The author would like to thank professor Andrzej Skowron for the inspiration and the useful remarks and also Aleksandra Janusz-Ochab and Marcin Szczuka for their support in writing and editing this paper. This research was supported by the grant N N516 368334 from Ministry of Science and Higher Education of the Republic of Poland and by the Innovative Economy Operational Programme 2007-2013 (Priority Axis 1. Research and development of new technologies).
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Janusz, A. (2008). Similarity Relation in Classification Problems. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_22
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