Attribute reduction algorithm based on discernibility for decision table

The attribute reduction algorithms of decision table based on discernible matrix are required to construct discernible matrix, which reduces efficiency of algorithms. In this paper, the relationship between attribute discernible matrix and its discernibility is first established for general information systems. Based on the idea that the equivalent discernible matrix has a same attribute reduction, existing matrices are modified and a formula of attribute discernibility associated with algebraic reduction for decision table is proposed. A heuristic attribute reduction algorithm based on attribute discernibility is presented. Experimental results indicate that the algorithm can more easily explore an optimal or sub-optimal reduction, and is efficient.

Rough Set Processing Model for Fuzzy Decision Tables with Weights

The classical rough set can not show the fuzziness and the importance of objects in decision procedure because it uses definite form to express each object. In order to solve this problem,this paper firstly introduces a special decision table in which each object has a membership degree to show its fuzziness and has been assigned a weight to show its importance in decision procedure. Then,the special decision table is studied and the relevant rough set model is provided. In the meantime,relevant definitions and theorems are proposed. On the above basis,an attribute reduction algorithm is presented. Finally,feasibility of the relevant rough set model and the presented attribute reduction algorithm are verified by an example.

Reducing Inconsistent Rules Based on Irregular Decision Table

In this paper, we study the problem of rule extraction from data sets using the rough set method. For inconsistent rules due to improper selection of split-points during discretization, and/or to lack of information, we propose two methods to remove their inconsistency based on irregular decision tables. By using these methods, inconsistent rules are eliminated as far as possible, without affecting the remaining consistent rules. Experimental test indicates that use of the new method leads to an improvement in the mean accuracy of the extracted rules.

A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.

Power transformer fault diagnosis model based on rough set theory with fuzzy representation

Objective Due to the incompleteness and complexity of fault diagnosis for power transformers,a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented.Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space.Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space.The fuzzy membership functions corresponding to the indicative regions,modelled by rules,are stored as cases.Results Diagnostic conclusions are made using a similarity measure based on these membership functions.Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis.Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.

Sand-Dust Storm Ensemble Forecast Model Based on Rough Set

To improve the accuracy of sand-dust storm forecast system, a sand-dust storm ensemble forecast model based on rough set (RS) is proposed. The feature data are extracted from the historical data sets using the self-organization map (SOM) clustering network and single fields forecast to form the feature values with low dimensions. Then, the unwanted attributes are reduced according to RS to discretize the continuous feature values. Lastly, the minimum decision rules are constructed according to the remainder attributes, namely sand-dust storm ensemble forecast model based on RS is constructed. Results comparison between the proposed model and the back propagation neural network model show that the sand-storm forecast model based on RS has better stability, faster running speed, and its forecasting accuracy ratio is increased from 17.1% to 86.21%.

Rule Generation Based on Dominance Matrices and Functions

Rough set theory has proved to be a useful tool for rule induction. But, the theory based on indiscernibility relation or similarity relation cannot induce rules from decision tables with criteria. Greco et al have proposed a new rough set approach based on dominance relation to handle the problems. The concept of dominance matrix is put forward and the dominance function is constructed to compute the minimal decision rules which are more general and applicable than the ones induced by the classical rough set theory. In addition, the methodology of simplification is presented to eliminate the redundancy in the rule set.

A Survey of Software Packages Used for Rough Set Analysis

Soft computing is a combination of methods that complement each other when dealing with ambiguous real life decision systems. Rough Set Theory (RST) is a technique used in soft computing that enhances the idea of classical sets to deal with incomplete knowledge and provides a mechanism for concept approximation. It uses reducts to isolate key attributes affecting outcomes in decision systems. The paper summarizes two algorithms for reduct calculation. Moreover, to automate the application of RST, different software packages are available. The paper provides a survey of packages that are most frequently used to perform data analysis based on Rough Sets. For benefit of researchers, a comparison of based on functionalities of those software is also provided.

An Incremental Rule Acquisition Algorithm Based on Rough Set

Rough Set is a valid mathematical theory developed in recent years, which hasthe ability to deal with imprecise, uncertain, and vague information. This paper presents a newincremental rule acquisition algorithm based on rough set theory. First, the relation of the newinstances with the original rule set is discussed. Then the change law of attribute reduction andvalue reduction are studied when a new instance is added. Follow, a new incremental learningalgorithm for decision tables is presented within the framework of rough set. Finally, the newalgorithm and the classical algorithm are analyzed and compared by theory and experiments.

Application of a Reliability Model Generator to a Pressure Tank System

A number of mathematical modelling techniques exist which are used to measure the performance of a given system, by assessing each individual component within the system. This can be used to determine the failure frequency or probability of the system. Software is available to undertake the task of analysing these mathematical models after an individual or group of individuals manually create the models. The process of generating these models is time consuming and reduces the impact of the model on the system design. One way to improve this would be to generate the model automatically. In this work, the procedure to automatically construct a model, based on Petri nets, for systems undergoing a phased-mission is applied to a pressure tank system, undertaking a four phase mission.

A transformation algorithm for incompatible information systems based on variable precision values

The variable precision rough set （VPRS） model extends the basic rough set （RS） theory with finite uni- verses and finite evaluative measures. VPRS is concerned with the equivalence and the contained relationship between two sets. In incompatible information systems, the inclusion degree and/3 upper （lower） approximation of the inconsistent equivalence class to the decision equivalence classes may be affected by the variable precision. The analysis of an example of incompatible decision table shows that there is a critical point in fl available-values region. In the new/3 range limited at the critical point, the incompatible decision table can be converted to the coordination decision table reliably. The method and its algorithm implement are introduced for the critical value search. The examples of the inconsistent equivalence class transformation are exhibited. The results illustrate that this algorithm is rational and precise.