Perception and Classification. A Note on Near Sets and Rough Sets

Quantitative rough sets based on subsethood measures

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

In this paper, we attempt revising the object-oriented rough set (for short, OORS) model proposed by the authors. The OORS model treats semi-structured data in the framework of rough sets by using structural hierarchies among objects, and semi-structured decision rules represent structural characteristics among objects. The revised OORS model we propose in this paper has almost the same representation ability of the previous model and the formulation of the revised model is simpler than the previous model.

Optimal approximations with Rough Sets and similarities in measure spaces

Monotonic Variable Consistency Rough Set Approaches

Representations of L-fuzzy rough approximation operators

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.

This chapter presents a generic scheme for generating prediction rules based on rough set approach for stock market prediction. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data, which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. For comparison, the results obtained using rough set approach were compared to that of artificial neural networks and decision trees. Empirical results illustrate that rough set approach achieves a higher overall prediction accuracy reaching over 97% and generates more compact and fewer rules than neural networks and decision tree algorithm.

Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design

Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining

Uncertainty management is necessary for real world applications, especially spatial data and geographic information systems. The egg-yolk method has proven useful for representing vague regions in spatial data. Rough sets have been shown to be an effective tool for data mining and uncertainty management in databases. In this initial work, we apply rough set definitions for topological relationships previously defined for the egg-yolk method for continuous space. We show that rough sets can be used to express and improve on topological relationships and concepts defined with the egg-yolk model, and extend it to work for discrete space through the use of rough set indiscernibility.

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

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