Attributes In Rough Set Theory Research Articles

A novel approach for discretization of continuous attributes in rough set theory

Discretization of continuous attributes is an important task in rough sets and many discretization algorithms have been proposed. However, most of the current discretization algorithms are univariate, which may reduce the classification ability of a given decision table. To solve this problem, we propose a supervised and multivariate discretization algorithm — SMDNS in rough sets, which is derived from the traditional algorithm naive scaler (called Naive). Given a decision table DT=(U,C,D,V,f), since SMDNS uses both class information and the interdependence among various condition attributes in C to determine the discretization scheme, the cuts obtained by SMDNS are much less than those obtained by Naive, while the classification ability of DT remains unchanged after discretization. Experimental results show that SMDNS is efficient in terms of the classification accuracy and the number of generated cuts. In particular, our algorithm can obtain a satisfactory compromise between the number of cuts and the classification accuracy.

Reducing Attributes in Rough Set Theory with the Viewpoint of Mining Frequent Patterns

The main objective of the Attribute Reduction problem in Rough Set Theory is to find and retain the set of attributes whose values vary most between objects in an Information System or Decision System. Besides, Mining Frequent Patterns aims finding items that the number of times they appear together in transactions exceeds a given threshold as much as possible. Therefore, the two problems have similarities. From that, an idea formed is to solve the problem of Attribute Reduction from the viewpoint and method of Mining Frequent Patterns. The main difficulty of the Attribute Reduction problem is the time consuming for execution, NP-hard. This article proposes two new algorithms for Attribute Reduction: one has linear complexity, and one has global optimum with concepts of Maximal Random Prior Set and Maximal Set.

The Application of Combination Forecasting Method in Total Power of Agriculture Machinery Based on RS

Forecast of agriculture machinery total power is a complicated non-linear system，combination forecasting can take full advantage of known information, to improve prediction accuracy .The relative data model between forecast objective and forecast model, and knowledge system and decision table was established respectively by means of converting continuous attribute values into discrete attribute values. Then, the weight of combination forecast model was calculated according to estimating dependence and significance of attributes in rough set theory, from this ,then constructed the combination forecasting model and conducted combination forecasting agriculture machinery total power in Heilongjiang province. The results showed that the forecast average error of combination forecast model is 3.13,which is lower than 3.71、5.25 and 3.63 of quadratic curve model, GM(1,1) and cubic exponent smooth model , and is also lower than 3.34 and 3.26 of the combinatorial forecast model based on the divergence coefficient method and Shapley value method.

New method for discretization of continuous attributes in rough set theory

A new method for discretization of continuous attributes is put forward to overcome the limitation of the traditional rough sets, which cannot deal with continuous attributes. The method is based on an improved algorithm to produce candidate cut points and an algorithm of reduction based on variable precision rough information entropy. With the guarantee of consistency of decision system, the method can reduce the number of cut points and improve efficiency of reduction. Adopting variable precision rough information entropy as measure criterion, it has a good tolerance to noise. Experiments show that the algorithm yields satisfying reduction results.