This monograph presents theory concerning interval-valued fuzzy calculus, especially aggregation operators among which there are placed recently introduced pos-aggregation functions and nec-aggregation functions. Moreover, applications of interval-valued fuzzy methods (and more generally interval modeling), especially mentioned so far interval-valued aggregation functions in classification are provided. The presented algorithms may support decision processes for example in medical diagnosis. It was shown that applying interval-valued methods make it possible to improve classification results in situation when there is a large number of missing values or there is a large number of attributes in the considered data sets. The mentioned methods were applied in a few experiments, involving machine learning methods, whose results are presented in this monograph. The obtained results of the described algorithms confirm their usefulness. They allow effective classification in the case of uncertainty (imprecise or incomplete information). The source codes of the presented classification algorithms (from Chaps. 4 and 5) are available at .
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