## 1 Introduction  
Nowadays, 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]. What is more, often the data are not well-defined, and are vague or imbalanced, which is an additional obstacle. Therefore, fuzzy techniques became the right solution. We may divide these techniques into two primary groups: those related to the classical type-1 fuzzy sets, introduced by [PERSON] [3] and those related to new concepts concerning type-2 fuzzy sets [4, 5]. In practice, 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. What is more, researchers have proved that interval type-2 fuzzy concepts are better able to handle uncertainties than type-1 fuzzy approaches [6, 7, 8, 9, 10, 11]. Interval type-2 fuzzy sets are very effective, as they provide better generalisations when it is difficult to determine the exact membership functions of the fuzzy sets applied.  
On the other hand, to deal with information issues, data discovery techniques, assumed as the computational process of discovering patterns in data, are widely used to induce knowledge. Information systems and rough sets, introduced by [PERSON] [12, 13, 14], are applied to represent knowledge. Therefore, it seems that the combination of fuzzy techniques and data discovery is very appropriate for the analysis of complex data such as medical data [15, 16].  
That is how the concept of fuzzy information systems came out. If each attribute of an information system is related to a fuzzy set, a fuzzy information system is defined [17]. Fuzzy information systems find their applications in the field of decision-making [18, 19]. In recent research, a novel multi-criteria approach with application in investments was proposed [20]. Many other applications also can be found concerning rule extraction [19, 21], feature selection and classification [22, 23, 24, 25]. Mathematical properties of fuzzy information systems are under consideration as well [17, 26]. The concept of rough sets was extended by analyzing the properties of lower and upper approximations of fuzzy sets [27, 28, 29, 30, 31] with corresponding practicable applications. For example, in the significant field of machine learning [32] or even medical image analyses, as an image segmentation approach [33].  
In our research, we introduce a fuzzy information system which induces type-1 fuzzy rules and transforms them into interval type-2 fuzzy rules. We assume the information function values to be fuzzy sets, but still applying the original definitions of indistinguishability relation and rough sets defined by [PERSON]. Our research experiments consist of investigating different fuzzification procedures, which determine the optimal fuzzy classifier.  
The rest of the paper is organized as follows: in Section 2, the benchmark data used and the classification methodology proposed are explained, in Section 3 the classification evaluation is presented, and finally, Sections 4 and 5 draw discussion and conclusions.