This paper presents an alternative way for constructing a topological space in an information system. Rough set theory for reasoning about data in information systems is used to construct the topology. Using the concept of an indiscernibility relation in rough set theory, it is shown that the topology constructed is a quasi-discrete topology. Furthermore, the dependency of attributes is applied...

In many real world applications, data are organized by coverings, not partitions. Covering-based rough sets have been proposed to cope with this type of data. Covering-based rough set theory is more general than rough set theory, then there is a need to employ sophisticated theories to make it more adaptive to real world application. The covering is one of core concepts in covering-based rough ...

Case-Based Reasoning systems retrieve cases using a similarity function based on the K-NN or some derivatives. These functions are sensitive to irrelevant, interacting or noisy features. Many similarity functions weigh the relevance of features to avoid this problem. This article proposes two weighting methods based on Rough Sets theory: Proportional Rough Sets and Dependence Rough Sets. Both w...

Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be consi...

The notion of rough sets, introduced by Z. Pawlak in 1982, is to capture impreciseness and indiscernibility of objects. The basic assumption of rough set theory is that human knowledge about a universe depends upon their capability to classify its objects. Classifications (or partitions) of a universe and equivalence relations defined on it are known to be interchangeable notions. So, for mathe...

The high social costs associated with bankruptcy have spurred searches for better theoretical understanding and prediction capability. In this paper, we investigate a hybrid approach to bankruptcy prediction, using a genetic programming algorithm to construct a bankruptcy prediction model with variables from a rough sets model derived in prior research. Both studies used data from 291 US public...

This paper compares two artificial intelligence methods the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the attribute (feature) transformations, which allows users to input formulas and create new attributes. Al...