Abstract
In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.
Highlights
Nowadays, machine learning and its applications are growing rapidly
We introduce a fuzzy information system which induces type-1 fuzzy rules and transforms them into interval type-2 fuzzy rules
To apply type-2 fuzzy sets in the fuzzy rule induction procedure described, we propose a simple extension
Summary
Machine learning and its applications are growing rapidly. Classification is one of the major machine learning problems. As there are various kinds of data, the need for robust techniques has become essential, especially in real-world applications [1,2]. We may divide these techniques into two primary groups: those related to the classical type-1 fuzzy sets, introduced by Zadeh [3] and those related to new concepts concerning type-2 fuzzy sets [4,5]. The interval type-2 fuzzy sets [6], a particular case of type-2 fuzzy sets, are commonly used for their reduced computational cost and easy implementation
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