Rough sets are efficient for data pre-processing in data mining. As a generalization of the linear independence in vector spaces, matroids provide wellestablished platforms for greedy algorithms. In this paper, we apply rough sets to matroids and study the contraction of the dual of the corresponding matroid. First, for an equivalence relation on a universe, a matroidal structure of the rough s...

Traditional rough set theory is only suitable for dealing with discrete variables and need data preprocessing. Neighborhood rough sets overcome these shortcomings with the ability to directly process numeric data. This paper modifies the attribute reduction method based on neighborhood rough sets, in which the attribute importance is combined with information entropy to select the appropriate a...

Kernelization is a formalization of preprocessing for combinatorially hard problems. We modify the standard definition for kernelization, which allows any polynomial-time algorithm for the preprocessing, by requiring instead that the preprocessing runs in a streaming setting and uses O(poly(k) log |x|) bits of memory on instances (x, k). We obtain several results in this new setting, depending ...

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...

A method for indicator selection is proposed in this paper. The method, which adopts the General Methodology and Design Research approach, consists of four steps: Problem Identification, Requirement Gathering, Indicator Extraction, and Evaluation. Rough Set approach also has been applied in the Indicator Extraction phase. This phase consists of 5 steps: Data selection, Data Preprocessing, Discr...

Data used in machine learning applications is prone to contain both vague and incomplete information. Many authors have proposed to use fuzzy rough set theory in the development of new techniques tackling these characteristics. Fuzzy sets deal with vague data, while rough sets allow to model incomplete information. As such, the hybrid setting of the two paradigms is an ideal candidate tool to c...