A Generic Scheme for Generating Prediction Rules Using Rough Sets

An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems

In this paper, a new anti-dumping early-warning system for the export of China's textile products is presented. The early-warning system based on Rough sets and neuro-fuzzy decision tree modelling method, which is different from traditional modelling methods. We can get reduced information table by rough set, which implies that the number of index and qualitative variables is reduced with no information loss through rough set approach. And then, neural networks-fuzzy decision tree (a fuzzy decision tree structure with neural like parameter adaptation strategy) improves FDT's classification accuracy and carry through anti- dumping early-warning more accuracy. The other new attempt is the setting of early-warning intervals. The result of the positive research indicated that this system is very valid for anti-dumping prediction and it will have a good application prospect in this area.

The integrated methodology of rough set theory and artificial neural network for business failure prediction

Monotonic Variable Consistency Rough Set Approaches

Weather has an important role in people's lives, such as agriculture, economics, socio-economic, disaster management, and finance. So, weather prediction is very important to be considered. In the prediction process we are often faced with the problem of data incompleteness. Therefore, it needs a proper classification algorithm that able to handle incomplete attribute values in the training data. In this paper, we use two approaches to handle incomplete data, namely are rough set and fuzzy rough set based algorithms. To test the performance of the two algorithms, we use meteorological data to classify rain or dry season. Conclusion of the study showed that the rough set approach is more efficient than the fuzzy rough sets approach. The advantage of fuzzy rough set approach can predict all the conditions that may occur, which can't be done by the rough set approach.

Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and BP neural network is studied. Since rough set theory can effectively simplify information, cut down the tagged dimension . This paper will be rough set theory and neural networks combined, using channel capacity of knowledge relative reduction algorithms to simplify the input information. Rough set theory is first used to process the sample data, and eliminate the redundant information, then reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole data fusion system. The effectiveness of the improved algorithm is demonstrated by an example compared with the traditional neural network system.

Rough sets theory (Pawlak 1980s) proved to be an excellent mathematical tool for the task of automated extraction of If –Then rule sets from table-organized data. In this paper, we employ an approach based on the relation of indiscernibility and Rough sets theory in comparison to the method based on pure classification. We have used a well-known ROSETTA software system. The main goal of this work is to compare rule set generated by ROSETTA and rule set generated by method based on pure classification. Comparison is conducted on real–life database from domain of medicine including recently discovered protein hormone Leptin.

Rough Set Theory (RST) is relatively new and powerful mathematical tool to deal with imperfect data (i.e. data with uncertainty and vagueness) which is primarily used for classification and decision making problems. On the other hand, Logistic regression (Logit) is mainly used in Social Sciences when dependent variable takes limited and categorical data value ranges. However, both RST and Logit regression are powerful predictable models that are used in wide range of applications such as medicine, military, banking, financial markets etc. RST uses approximations and implications as two formal tools to deal with vagueness whereas Logit regression is severely constrained to deal with vague and imprecise data. Yet, both these methodologies are used to classify the object which is the key issue in decision making. This research paper compares these two tools on a common dataset. SPSS 17.0 software is used to run the Logit regression and Rose 2 software is used for analysis of Rough Set. One of the important finding of this comparison is that attributes in core of the data set under the rough set approach are similar to the most significant predictors of logistic regression model. This indicates that the significant attributes deducted by these two methodologies are similar. It is demonstrated that rough set is much more superior tool to classify the objects as compared to logistic regression. One of the important outcomes of this research is that degree of accuracy is much higher in rough set as compared to logistic regression thereby establishing the supremacy of rough set as a better decision making tool.

Financial time series forecasting using rough sets with time-weighted rule voting

Rough set theory is a well established tool for dealing with inconsistent data. The dependencies among the attributes, their significance, and evaluation can easily be performed using intelligent data analysis tool viz., rough set theory. The objective of this article is to modify the existing stock market predictive model based on rough set approach by A.E Hassanien et al. and to construct a data model that would generate fewer number of decision rules. Moreover the results obtained from the proposed data model are compared with well-known software tool Rough set Exploration system 2.2 popularly known as RSES 2.2. It is shown that the proposed model has a higher overall accuracy rate and generates more compact and fewer rules than RSES 2.2. Rough confusion matrix is used to evaluate the predicted classification performances. The effectiveness of this data model is demonstrated on data set consisting of daily movements of a stock traded in Kuwait Stock Exchange spanning over a period of five years.

A hybrid approach of DEA, rough set and support vector machines for business failure prediction

Reducts and constructs in classic and dominance-based rough sets approach

A rough set approach to intuitionistic fuzzy soft set based decision making

Rough set theory has been proposed by Pawlak in the early 80s to deal with inconsistency problems following from information granulation. It operates on an information table composed of a set U of objects described by a set Q of condition and decision attributes. Decision attributes make a partition of U into decision classes. Basic concepts of rough set theory are: indiscernibility relation on U, lower and upper (rough) approximations of decision classes, dependence and reduction of attributes from Q, and decision rules induced from rough approximations of decision classes. The original rough set idea was failing, however, to handle preferential ordering of domains of attributes (scales of criteria), as well as preferential ordering of decision classes. In order to deal with multiple criteria decision problems a number of methodological changes to the original rough set theory were necessary. The main change is the substitution of the indiscernibility relation by a dominance relation, which permits approximation of ordered sets. In multiple criteria decision context, the information table is composed of decision examples given by a decision maker. The Dominance-based Rough Set Approach (DRSA) applied to this information table results with a set of decision rules, being a preference model of the decision maker. It is more general than the classical multiple attribute utility model or outranking model, and it is more understandable because of its natural syntax. In this chapter, after recalling the classical rough set approach and DRSA, we review their fuzzy set extensions. Moreover, we characterize the dominance-based rough approximation of a fuzzy set, and we show that the classical rough approximation of a crisp set is its particular case. In this sense, DRSA is also relevant in the case where preferences are not considered, but just a kind of monotonicity relating values of different attributes is meaningful for the analysis of data at hand. In general terms, monotonicity concerns relationship between different aspects of a phenomenon described by data: for example, “the larger the house, the higher its price” or “the closer the house to the city centre, the higher its price”. In this perspective, DRSA gives a very general framework for reasoning about data using only monotonicity relationships.

In this paper we present a new approach to rough set approximations that permits to distinguish between two kinds of “imperfect” knowledge in a joint framework: on one hand, vagueness, due to imprecise knowledge and uncertainty typical of fuzzy sets, and on the other hand, ambiguity, due to granularity of knowledge originating from the coarseness typical of rough sets. The basic idea of our approach is that each concept is represented by an orthopair, that is, a pair of disjoint sets in the universe of knowledge. The first set in the pair contains all the objects that are considered as surely belonging to the concept, while the second set contains all the objects that surely do not belong to the concept. In this context, following some previous research conducted by us on the algebra of rough sets, we propose to define as rough approximation of the orthopair representing the considered concept another orthopair composed of lower approximations of the two sets in the first orthopair. We shall apply this idea to the classical rough set approach based on indiscernibility, as well as to the dominance-based rough set approach. We discuss also a variable precision rough approximation, and a fuzzy rough approximation of the orthopairs. Some didactic examples illustrate the proposed methodology.