Attribute reduction is one important part researched in rough set theory. A reduct from a decision table minimal subset of the conditional attributes which provide same information for classification purposes as entire available attributes. The task high dimensional could be solved faster if reduct, instead original whole attributes, used. In this paper, we propose computing algorithm using att...

Feature selection has been widely discussed as an important preprocessing step in data mining applications since it reduces a model's complexity. In this paper, limitations of several representative reduction methods are analyzed firstly, and then by distinguishing consistent objects form inconsistent objects, decision inclusion degree and its probability distribution function as a new measure ...

RULE REDUCTS BASED ON ROUGH SET THEORY Şahin Emrah AMRAHOV Computer Engineering Department , Ankara University, Ankara, Turkey Fatih AYBAR Computer Engineering Department, Ankara University, Ankara, Turkey Serhat DOĞAN Economics Department, Bilkent University, Ankara, Turkey In this paper it is considered rule reduct generation problem, based on Rough Set Theory. Rule Reduct Generation (RG) and...

Attribute reduction with fuzzy rough set is an effective technique for selecting most informative attributes from a given realvalued dataset. However, existing algorithms for attribute reduction with fuzzy rough set have to re-compute a reduct from dynamic data with sample arriving where one sample or multiple samples arrive successively. This is clearly uneconomical from a computational point ...

Development of an efficient real time intrusion detection system (IDS) has been proposed in the paper by integrating Q-learning algorithm and rough set theory (RST). The objective of the work is to achieve maximum classification accuracy while detecting intrusions by classifying NSL-KDD network traffic data either ‘normal’ or ‘anomaly’. Since RST processes discrete data only, by applying cut op...

Breast cancer is the most common malignant tumor found among young and middle aged women. Feature Selection is a process of selecting most enlightening features from the data set which preserves the original significance of the features following reduction. The traditional rough set method cannot be directly applied to deafening data. This is usually addressed by employing a discretization meth...

As one of the key topics in development neighborhood rough set, attribute reduction has attracted extensive attentions because its practicability and interpretability for dimension or feature selection. Although random sampling strategy been introduced to avoid overfitting, uncontrollable may still affect efficiency search reduct. By utilizing inherent characteristics each label, Multi-label le...

The problem of improving rough set based expert systems by modifying a notion of reduct is discussed. The notion of approximate reduct is introduced, as well as some proposals of quality measure for such a reduct. The complete classifying system based on approximate reducts is presented and discussed. It is proved that the problem of finding optimal set of classifying agents based on approximat...

The symbolic value partition problem is more general than the reduct problem and more complicated than the discretization problem. In this paper, we point out that the symbolic value partition problem can be converted into a series of reduct problems and propose an algorithm called reduction based symbolic value partition (RBSVP) whose all possible outputs form the set of all partition reducts....