@INPROCEEDINGS { Rsds4612,
author = {Honko, Piotr}, 
title = {Classification of Complex Structured Objects on the Base of Similarity Degrees}, 
booktitle = {Proceedings of the International Conference on Rough Sets and Emerging Intelligent Systems Paradigms (RSEISP'2007)}, 
conference = {International Conference on Rough Sets and Emerging Intelligent Systems Paradigms}, 
series =  = {Lecture Notes in Artificial Intelligence}, 
volume = 4585, 
pages = {553--563}, 
publisher = {Springer-Verlag}, 
address = {Heidelberg - Berlin, Germany}, 
year = 2007, 
editor = {Peters, James F. and Rybinski, Henryk and Skowron, Andrzej}, 
isbn = {978-3-540-73450-5}, 
abstract = {In the paper, we propose an algorithm for classification of complex structured objects. The objects, expressed in a first-order logic (FOL) language, are positive and negative examples of a target relation. In the process of searching for a classification pattern, a similarity measure and some notions of rough set theory are applied. We search for the pattern being a similarity degree of examples and satisfying two conditions: the number of positive examples similar at least to the degree to other positive examples is highest and the number of negative examples similar at least to the degree to positive examples is lowest. The obtained set of similar examples corresponds to the lower or to the upper approximation of a set of all positive examples. The found similarity degree is applied in classification of new examples. An example is classified as positive if it belongs to the approximation computed with respect to the degree, and it is classified as negative, otherwise.}, 
keywords = {first-order logic, rough sets, similarity measures, }, 
}