Rough Set Technique Research Articles

Eigen-Level Data Fusion Model by Integrating Rough Set and Probabilistic Neural Network for Structural Damage Detection

In this paper, a new eigen-level data fusion model, whereby rough set data and a probabilistic neural network (PNN) are integrated using a data fusion technique, is proposed for structural damage detection. This model is used for structural damage detection and identification, particularly for cases where the measurement data has many uncertainties. More specifically, structural modal parameters derived from vibration responses are first discretized by the K-means clustering technique and the rough set technique is then employed to deal with the great volume of data and to extract optimal feature parameters. After that, the processed data and information are input to the fusion centre of the data fusion technique and fused with the PNN to give a fusion-based damage detection result. To verify the proposed method, two numerical examples are presented to identify both single and multi-damage case patterns. The effects of measurement noise and of non pre-processed rough set data on the damage detection results are also discussed. The results show that the proposed model not only has good damage detection capability and noise tolerance, but also significantly reduces data storage memory requirements and saves runtime as a consequence of the data fusion processing.

An agent model for rough classifiers

This paper proposes a new agent-based approach in rough set classification theory. In data mining, the rough set technique is one classification technique. It generates rules from a large database and has mechanisms to handle noise and uncertainty in data. However, producing a rough classification model or rough classifier is computationally expensive, especially in its reduct computation phase: this is an NP-hard problem. These problems have brought about the generation of large amount of rules and high processing time. We solve these problems by embedding an agent-based algorithm within the rough modelling framework. In this study, the classifiers are based on creating agents within the main modelling processes such as reduct computation, rules generation and attribute projections. Four main agents are introduced: the interaction agent, weighted agent, reduction agent and default agent. We propose a heuristic for the default agent to control its searching activity. Experiments show that the proposed method significantly reduces the running time and the number of rules while maintaining the same classification accuracy.

The Application of Rough Set Technique for Missing Data

In this paper, a missing data classification algorithms based on rough technique is proposed, and the complexity of the algorithms is analyzed, finally a missing data classification experiment with a typical dataset is conducted. The result of experimentation shows the algorithms not only can effectively improve the accuracy and efficiency of classification while enormously reducing the number of attributes, but also have the good performance on noises control.

Technical decomposition approach of critical to quality characteristics for Product Design for Six Sigma

AbstractProduct Design for Six Sigma (DFSS) approach is a structural and disciplined methodology driven by critical to quality characteristics (CTQs). How to identify and decompose the CTQs is the kernel part in the DFSS process. Traditional method only depends on the quality function deployment (QFD) matrix to flow down CTQs roughly. The paper puts forward a novel technical approach for CTQs decomposition from customer requirements into critical technical parameters based on the relational tree. Specifically, this approach emphasizes the systematic process and quantitative computation on quality relation weight. In order to specify the object of product DFSS, the connotation and evolution model of CTQs are created first. Then along the product development process, a decomposition measure for relational tree of CTQs is studied based on the functional and physical trees in Axiomatic Design (AD). And the quality relation weight computation of its nodes by means of Rough Set and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is explored. Finally, an application on a car body noise vibration harshness (NVH) improvement, as an example, is given, and the decomposition process of NVH related with the functional and physical trees as well as its node weights computation algorithm are expounded in detail. Copyright © 2009 John Wiley & Sons, Ltd.

Uncertainty modeling for database design using intuitionistic and rough set theory

This paper introduces the intuitionistic rough set and intuitionistic rough relational and rough object oriented database models. Rough set, fuzzy set, and intuitionistic set uncertainty management are discussed and compared, and the model based on intuitionistic and rough sets developed here is applied to databases. The intuitionistic rough set database models draw benefits from both the rough set and intuitionistic techniques, providing greater management of uncertainty for databases applications in a less than certain world.

A new method for short-term electricity load forecasting

Accurate forecasting of short-term electricity load is an important issue in the electricity industry. This paper proposes a new forecasting model by integrating the support vector machines (SVMs) forecasting technique and rough sets (RSs) with reduced attributes using evolutionary algorithms (EAs). Simulation results show that this new model can improve the prediction accuracy, speed the convergence and require less computational effort in comparison with another two methods, namely the traditional SVM model and a model combining the SVMs and simulated annealing algorithms (SVMSA). This improvement is related to fact that the RS techniques can reduce the SVM input variables and improve the convergence.

Induction of multiple fuzzy decision trees based on rough set technique

The integration of fuzzy sets and rough sets can lead to a hybrid soft-computing technique which has been applied successfully to many fields such as machine learning, pattern recognition and image processing. The key to this soft-computing technique is how to set up and make use of the fuzzy attribute reduct in fuzzy rough set theory. Given a fuzzy information system, we may find many fuzzy attribute reducts and each of them can have different contributions to decision-making. If only one of the fuzzy attribute reducts, which may be the most important one, is selected to induce decision rules, some useful information hidden in the other reducts for the decision-making will be losing unavoidably. To sufficiently make use of the information provided by every individual fuzzy attribute reduct in a fuzzy information system, this paper presents a novel induction of multiple fuzzy decision trees based on rough set technique. The induction consists of three stages. First several fuzzy attribute reducts are found by a similarity based approach, and then a fuzzy decision tree for each fuzzy attribute reduct is generated according to the fuzzy ID3 algorithm. The fuzzy integral is finally considered as a fusion tool to integrate the generated decision trees, which combines together all outputs of the multiple fuzzy decision trees and forms the final decision result. An illustration is given to show the proposed fusion scheme. A numerical experiment on real data indicates that the proposed multiple tree induction is superior to the single tree induction based on the individual reduct or on the entire feature set for learning problems with many attributes.

Soft computing techniques for web services brokering

To enhance and improve the interoperability of meteorological Web Services, we are currently developing an Integrated Web Services Brokering System (IWB). IWB uses a case-based classifier to automatically discover Web Services. In this paper, we explore the use of rough set techniques for selecting features prior to classification. We demonstrate the effectiveness of this feature technique by comparing it with a leading non-rough set (Information Gain) feature selection technique.

Improved Classification for Problem Involving Overlapping Patterns

The support vector machine has received wide acceptance for its high generalization ability in real world classification applications. But a drawback is that it uniquely classifies each pattern to one class or none. This is not appropriate to be applied in classification problem involves overlapping patterns. In this paper, a novel multi-model classifier (DR-SVM) which combines SVM classifier with kNN algorithm under rough set technique is proposed. Instead of classifying the patterns directly, patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. The classification operation is carried out on these new sets. Simulation results on synthetic data set and benchmark data sets indicate that, compared with conventional classifiers, more reasonable and accurate information about the pattern's category could be obtained by use of DR-SVM.

An efficient bit-based feature selection method

Feature selection is about finding useful (relevant) features to describe an application domain. Selecting relevant and enough features to effectively represent and index the given dataset is an important task to solve the classification and clustering problems intelligently. This task is, however, quite difficult to carry out since it usually needs a very time-consuming search to get the features desired. This paper proposes a bit-based feature selection method to find the smallest feature set to represent the indexes of a given dataset. The proposed approach originates from the bitmap indexing and rough set techniques. It consists of two-phases. In the first phase, the given dataset is transformed into a bitmap indexing matrix with some additional data information. In the second phase, a set of relevant and enough features are selected and used to represent the classification indexes of the given dataset. After the relevant and enough features are selected, they can be judged by the domain expertise and the final feature set of the given dataset is thus proposed. Finally, the experimental results on different data sets also show the efficiency and accuracy of the proposed approach.

Learning fuzzy rules from fuzzy samples based on rough set technique

Although the traditional rough set theory has been a powerful mathematical tool for modeling incompleteness and vagueness, its performance in dealing with initial fuzzy data is usually poor. This paper makes an attempt to improve its performance by extending the traditional rough set approach to the fuzzy environment. The extension is twofold. One is knowledge representation and the other is knowledge reduction. First, we provide new definitions of fuzzy lower and upper approximations by considering the similarity between the two objects. Second, we extend a number of underlying concepts of knowledge reduction (such as the reduct and core) to the fuzzy environment and use these extensions to propose a heuristic algorithm to learn fuzzy rules from initial fuzzy data. Finally, we provide some numerical experiments to demonstrate the feasibility of the proposed algorithm. One of the main contributions of this paper is that the fundamental relationship between the reducts and core of rough sets is still pertinent after the proposed extension.

Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques

Early work on case-based reasoning (CBR) reported in the literature shows the importance of soft computing techniques applied to different stages of the classical four-step CBR life cycle. This correspondence proposes a reduction technique based on rough sets theory capable of minimizing the case memory by analyzing the contribution of each case feature. Inspired by the application of the minimum description length principle, the method uses the granularity of the original data to compute the relevance of each attribute. The rough feature weighting and selection method is applied as a preprocessing step prior to the generation of a fuzzy rule system, which is employed in the revision phase of the proposed CBR system. Experiments using real oceanographic data show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules

Deciding the financial health of dot-coms using rough sets

We conducted an empirical investigation of dot-coms from a financial perspective. Data from the financial statements of 240 such businesses was used to compute financial ratios and the rough sets technique was used to evaluate whether the financial ratios could predict financial health of them based on available data. The most predictive financial ratios were identified and interesting rules concerning the financial ratios and financial health of dot-coms were discovered. It was shown that rough sets performed a satisfactory job of predicting financial health and were more suitable for detecting unhealthy dot-coms than healthy ones.

A rough set-based case-based reasoner for text categorization

This paper presents a novel rough set-based case-based reasoner for use in text categorization (TC). The reasoner has four main components: feature term extractor, document representor, case selector, and case retriever. It operates by first reducing the number of feature terms in the documents using the rough set technique. Then, the number of documents is reduced using a new document selection approach based on the case-based reasoning (CBR) concepts of coverage and reachability. As a result, both the number of feature terms and documents are reduced with only minimal loss of information. Finally, this smaller set of documents with fewer feature terms is used in TC. The proposed rough set-based case-based reasoner was tested on the Reuters21578 text datasets. The experimental results demonstrate its effectiveness and efficiency as it significantly reduced feature terms and documents, important for improving the efficiency of TC, while preserving and even improving classification accuracy.

A novel feature selection method for large-scale data sets1

Feature selection is about finding useful (relevant) features to describe an application domain. The problem of finding the minimal subsets of features that can describe all of the concepts in the given data set is NP-hard. In the past, we had proposed a feature selection method, which originated from rough set and bitmap indexing techniques, to select the optimal (minimal) feature set for the given data set efficiently. Although our method is sufficient to guarantee a solution's optimality, the computation cost is very high when the number of features is huge. In this paper, we propose a nearly optimal feature selection method, called bitmap-based feature selection method with discernibility matrix, which employs a discernibility matrix to record the important features during the construction of the cleansing tree to reduce the processing time. And the corresponding indexing and selecting algorithms for such feature selection method are also proposed. Finally, some experiments and comparisons are given and the result shows the efficiency and accuracy of our proposed method.

The application of rough sets analysis in activity-based modelling. Opportunities and constraints

Abstract This paper reports on an attempt to introduce the rough sets (RS) technique into an activity-based modelling approach. Since their introduction in 1981 by Pawlak, rough sets have already been successfully applied in a series of research domains. Surprisingly, though the approach has never been applied in modelling travel choice patterns. In order to investigate the opportunities and constraints of the rough sets approach in activity-based travel modelling, we use data from a Belgian travel diary survey, and analyse these data by means of the rosetta -software. This software package was originally developed by Ohrn [Discernibility and Rough Sets in Medicine: Tools and Applications (1999)]. In total, two case studies are conducted through which the performance of the rough sets approach for travel pattern generation, classification and choice prediction will be ascertained. Although the overall results may seem not that promising, interesting conclusions for future research can be drawn.

Mining pinyin-to-character conversion rules from large-scale corpus: a rough set approach.

This paper introduces a rough set technique for solving the problem of mining Pinyin-to-character (PTC) conversion rules. It first presents a text-structuring method by constructing a language information table from a corpus for each pinyin, which it will then apply to a free-form textual corpus. Data generalization and rule extraction algorithms can then be used to eliminate redundant information and extract consistent PTC conversion rules. The design of our model also addresses a number of important issues such as the long-distance dependency problem, the storage requirements of the rule base, and the consistency of the extracted rules, while the performance of the extracted rules as well as the effects of different model parameters are evaluated experimentally. These results show that by the smoothing method, high precision conversion (0.947) and recall rates (0.84) can be achieved even for rules represented directly by pinyin rather than words. A comparison with the baseline tri-gram model also shows good complement between our method and the tri-gram language model.

Rough set analysis of a general type of fuzzy data using transitive aggregations of fuzzy similarity relations

In this paper, we initially present and compile the tools and theories that are necessary for a Rough Set analysis of a general type of fuzzy data. From this study, we have developed two original contributions that can enhance the application of Fuzzy Rough Sets techniques to data that may be affected by several sources of uncertainty in its measurement process. For this type of analysis, we have first considered the construction of homogeneous granules in which reflexivity, symmetry and a certain transitivity property are satisfied. This has encouraged us to study the application of T-similarity relations in this framework and to examine the available theorems that permit us to aggregate this type of relations. As a part of this study, we have also developed a new theorem that allows us to use the dual of the generalized means as an aggregation operator in the Fuzzy Rough Sets context. Another theoretical development that tries to respond to a practical problem is the concept of β-precision aggregation. We use this new notion to export Ziarko's Variable Precision Rough Set model to the analysis of fuzzy data, a generalization that becomes a necessity for the Fuzzy Rough Sets analysis of large databases. The utility of this approach is demonstrated with an application example in the Vehicle database donated by the Turing Institute.

Rough Sets and Probability Masses for Dempster-Shafer Data Fusion at a Traffic Management Center

The Dempster–Shafer data-fusion technique as affected by probability masses as a result of sensor selection and probability masses distribution is investigated. Dempster–Shafer inference is a statistically based data-classification technique for detecting traffic events that affect normal traffic operations. It is used when data sources contribute discontinuous and incomplete information such that no single data source can produce a predominantly high probability of certainty for identifying the most probable event. To help in the selection of appropriate sensors and probability masses, a rough-sets data-mining technique in support of Dempster–Shafer inference was proposed and tested. The basic rough-sets technique is introduced, and a numerical example is given to explain its applications. Field testing of the rough-sets technique showed that it can reasonably and systematically process a large amount of traffic information, as an alternative to relying on the intuition of traffic operators and system managers. Because it allows easy maintenance and update of estimated probability masses, this technique is suitable for large-scale applications at the traffic management center.

Fuzzy rough set techniques for uncertainty processing in a relational database

Fuzzy rough set techniques for uncertainty processing in a relational database