Classical structure of rough set theory was first formulated by Z. Pawlak in [6]. The foundation of its object classification is an equivalence binary relation and equivalence classes. The upper and lower approximation operations are two core notions in rough set theory. They can also be seenas a closure operator and an interior operator of the topology induced by an equivalence relation on a u...

Quality function deployment (QFD) has been widely recognized as an effective means to develop quality products that can maximize customer satisfactions. This paper presents a novel extension to the fuzzy QFD methodology using rough set theory, with the aim to facilitate decision making in the early stages of product development and lead to the establishment of a QFD-based expert system for prod...

Among the huge number of attributes or features present in real-life data sets, only a small fraction of them are effective to represent the data set accurately. Prior to analysis of the data set, selecting or extracting relevant and significant features is an important preprocessing step used for pattern recognition, data mining, and machine learning. In this regard, a novel dimensionality red...

In digital-based information boom, the fuzzy covering rough set model is an important mathematical tool for artificial intelligence, and how to build the bridge between the fuzzy covering rough set theory and Pawlak’s model is becoming a hot research topic. In this paper, we first present the γ−fuzzy covering based probabilistic and grade approximation operators and double-quantitative approxim...

Based on rough sets, fuzzy set theory gives a model of information retrieval. "Contains" relationship reflects a match between the set of documents and user queries using fuzzy set theory, and its inclusion degree to achieve the sort of document sets of search results. The use of rough set equivalence relation reflects the correlation between keywords, achieve synonyms retrieve. Compared with t...

A generalized hybrid unsupervised learning algorithm, which is termed as rough-fuzzy possibilistic c-means (RFPCM), is proposed in this paper. It comprises a judicious integration of the principles of rough and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy ...

Many rule induction algorithms are unable to cope with high dimensional descriptions of input features. To enable such techniques to be effective, a redundancy-removing step is usually carried out beforehand. Rough Set Theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a crisp dataset; important information may be lost as a result of quantiz...