A New Radar Emitter Recognition Method Based on Variable Precision Rough Set Model

The variable precision rough set (VPRS) model generalizes the Pawlak rough set model with variable parameters. The Bayesian rough set (BRS) model improves the VPRS model with non-parametric modification by using the prior probability as a reference. This paper presents two research results related to rough set model and rough vague sets. One is a modification to the Bayesian rough set model and the other is a modification to rough vague sets. First, the Bayesian rough set model is analyzed and discussed. Second, a modification to this model is proposed and verified. Finally, a modification to rough vague sets is presented and its related properties are discussed..

A generalization of the original idea of rough sets and variable precision rough sets is introduced. This generalization is based on the concept of absolute and relative rough membership. Similarly to variable precision rough set model, the generalization called parameterized rough set model, is aimed at modeling data relationships expressed in terms of frequency distribution rather than in terms of a full inclusion relation used in the classical rough set approach. However, differently from variable precision rough set model, one or more parameters modeling the degree to which the condition attribute values confirm the decision attribute value, are considered. The properties of this extended model are investigated and compared to the classical rough set model and the variable precision rough set model.

An illustration of variable precision rough sets model: an analysis of the findings of the UK Monopolies and Mergers Commission

Traditional rough set of noise data, the lack of adaptability, lack of flexibility or robustness, for the engineering data can not distinguish between equivalence classes of edge region of overlap with the collection, resulting in loss of many valuable engineering information. Strong noise in the actual engineering data over-fitting due to reduced ability to distinguish the object, its limitations limits its further application. In this paper, variable precision rough set model (VPRS, the variable precision Rough set Model), allows a certain threshold level of classification rate exists, in order to better solve the engineering data, no functional relationship between attributes of interference data classification problem, it Overcome the standard rough set model on the shortcomings of the data is too sensitive to noise, thereby enhancing the data analysis and processing robustness. Finally, simulation results show that using variable precision rough set theory to solve the data uncertainty of similar projects under the influence of the extraction is feasible.

Variable precision rough set model is the extension of rough set model, which introduce theβparameter based on the basic rough set theory, that is, allows a certain degree of misclassification rate existing. This paper presents the web mining algorithm based on variable precision rough set in e-commerce, analyzes the limitation of the reduction methods based on variable precision rough set theory. The experiment shows the CPU Time in the attribute numbers, indicating that VPRS is superior to normal algorithm in web mining in e-commerce. The experiments show the algorithm could flexibly and dynamically web mining and therefore provide a valuable addition to the field.

PurposeThe purpose of this paper is to acquire doubly variable precision‐based knowledge rules from incomplete decision tables (IDTs) in the framework of pansystems methodology. It suggests a new variable precision limited tolerance – a special pansystems relation – rough set model with precision inclusion and a reduct procedure in which it overcomes the non‐monotony in forming tolerance classes when reducing an attribute from attribute set.Design/methodology/approachThrough introducing variable precision and limited tolerance relation in IDT, it constructs symmetric binary relation, dissimilar to non‐symmetric relation proposed by others, and then forms tolerance classes. It proposes a new reduction procedure with absolute value calculation to avoid tolerance classes being non‐monotone. Using variable inclusion, it obtains lower and upper approximations with noises.FindingsTolerance classes are not monotone with the reduction of attribute from attribute set in the proposed variable precision and limited tolerance relation, but it remains symmetry. Proposed reduction procedure with absolute value calculation is a new approach in adjudging whether a reduct equals to the original whole attribute set within a error range or not.Practical implicationsUsing variable precision and limited rough set model with variable inclusion to mine deep knowledge from IDT is a paradise in knowledge discovery in dealing with non‐determinative and vague problems.Originality/valueThe formation of symmetric tolerance relation is natural. The reduction procedure with absolute value calculation is new and not similar to those existed in literatures.

In order to effectively handle the real-valued data sets in practice, it is valuable from theoretical and practical aspects to combine fuzzy rough set and variable precision rough set so that a powerful tool can be developed. That is, the model of fuzzy variable precision rough set, which not only can handle numerical data but also is less sensitive to misclassification and perturbation,In this paper, we propose a new variable precision rough fuzzy set by introducing the variable precision parameter to generalized rough fuzzy set, i.e., the variable precision rough fuzzy set based on general relation. We, respectively, define the variable precision rough lower and upper approximations of any fuzzy set and it level set with variable precision parameter by constructive approach. Also, we present the properties of the proposed model in detail. Meanwhile, we establish the relationship between the variable precision rough approximation of a fuzzy set and the rough approximation of the level set for a fuzzy set. Furthermore, we give a new approach to uncertainty measure for variable precision rough fuzzy set established in this paper in order to overcome the limitations of the traditional methods. Finally, some numerical example are used to illuminate the validity of the conclusions given in this paper.

Variable Precision Rough Set (VPRS) model is one of the most important extensions of the Classical Rough Set (RS) theory. It employs a majority inclusion relation mechanism in order to make the Classical RS model become more fault tolerant, and therefore the generalization of the model is improved. This paper can be viewed as an extension of previous investigations on attribution reduction problem in VPRS model. In our investigation, we illustrated with examples that the previously proposed reduct definitions may spoil the hidden classification ability of a knowledge system by ignoring certian essential attributes in some circumstances. Consequently, by proposing a new ß-consistent notion, we analyze the relationship between the structures of Decision Table (DT) and different definitions of reduct in VPRS model. Then we give a new notion of /3-complement reduct that can avoid the defects of reduct notions defined in previous literatures. We also supply the method to obtain the /3- complement reduct using a decision table splitting algorithm, and finally demonstrate the feasibility of our approach with sample instances.

Variable precision rough set model, as an important probabilistic approach to rough set theory, can deal with many practical problems which involve noise data and cannot be effectively handled by Pawlak's rough set model. Generally, rough set theory based knowledge reduction includes attribute reduction and attribute value reduction. Attribute reduction in variable precision rough set model has been attracted many researchers' attentions. However, attribute value reduction in variable precision rough set model was rarely discussed. In this paper, an approach to attribute value reduction in variable precision rough set model is presented, with which the redundant information in the given decision table can be effectively removed and the properties of the acquired rules, namely deterministic or probabilistic, can be preserved well.

In this paper, we study variable precision rough set models based on multiple decision tables. The models can control the admissible level of classification error in each table, the ratio of supporting decision tables to all decision tables and the ratio of opposing decision tables to all decision tables. As the classical rough set model plays a key role in analysis of decision tables such as reduction, rule induction, etc., the proposed variable precision rough set models will play a key role in analysis of multiple decision tables.

An integration method of variable precision rough set and neural network for fault diagnosis is presented and used in rotary machinery fault diagnosis. The method integrates the ability of variable precision rough set on reduction of diagnosis information system and that of neural network for fault classification. Typical faults of rotating machinery were simulated in our rotor test-bed. The power spectrum data are used as rotating machinery fault diagnosis signal. For inconsistent data and noise data in power spectrum, variable precision rough set model allows a flexible region of lower approximations by precision variables. By attribute reduction based on variable precision rough set, redundant attributes are identified and removed. The reduction results are used as the input of neural network. The diagnosis results show that the proposed approach for input dimension reduction in neural network is very effective and has better learning efficiency and diagnosis accuracy.

As an effective tool for knowledge acquisition, attribute reduction is one of the key issues in rough set theory. In current research, most researchers choose to reduce redundant attributes as many as possible through some attribution reduction algorithms. However, the misclassification cost induced by attribute reduction is ignored. Thus, it is worth studying that how to reduce redundant attributes based on preserving the misclassification cost. Firstly, in this paper, the degree of minimum misclassification is defined. Then, by introducing decision process into variable precision rough set, a new model which is based on minimum misclassification cost with variable precision rough set (VPRS) is proposed. Moreover, based on the minimum misclassification cost, a heuristic attribute reduction algorithm is proposed. Finally, the simulation result shows that a feasible and reliable set of attributes can be obtained with our algorithm. These results further enrich attribute reduction to effectively deal with the uncertain classification problems.

Weighting formula and feature selection are key preprocessing in text classifying and mining. We analyze the drawbacks of weighting formula based on inverse document frequency and present a novel feature weighting and selecting method based on variable precision rough set model. Inverse document frequency (IDF) doesn't take the classification information into account and the criterion based on IDF is not monotonous with the contribution that a feature makes to classification, which decreases the classifier's performance. The measure of classification quality based on variable rough set model can deal with complex classification. It measures the contribution a feature makes to classification. It is introduced as a criterion for feature selecting and weighting in text classification. We name it as TFACQ. The experimental results show that the weighting formula and feature selection based on TFACQ have greatly improved the performance.

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) is the extension of the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The generic Rough Set Inductive Logic Programming (gRS-ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS-ILP model is extended to the VPRSILP model by including features of the VPRS model. The VPRSILP model is further extended in this paper to include future test cases. This model is applied to Web usage mining and an illustrative experiment is presented.

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The generic Rough Set Inductive Logic Programming (gRS‐ILP) model provides a framework for ILP when the setting is imprecise and any induced logic program will not be able to distinguish between certain positive and negative examples. The gRS‐ILP model is extended in this paper to the VPRSILP model by including features of the VPRS model. The VPRSILP model is applied to strings and an illustrative experiment on transmembrane domains in amino acid sequences is presented.