Application Of Rough Set Theory Research Articles

Application of rough set theory in apple disease diagnosis

This paper based on rough set, the original data, attribute reduction, value reduction, the rule extraction, the final diagnosis model building apple’s disease, disease diagnosis for apple to provide practical diagnostic tool.

A Rough Set Based Model in Water Quality Analysis

Due to pollution caused by the expansion of human activities and economic development, water quality has gradually deteriorated in many areas of the world. Therefore, analysis of water quality becomes one of the most essential issues of modern civilization. Integrated interdisciplinary modeling techniques, providing reliable, efficient, and accurate representation of the complex phenomenon of water quality, have gained attention in recent years. With the ability to deal with both numeric and nominal information, and express knowledge in a rule-based form, the Rough Set Theory (RST) has been successfully employed in many fields. However, the application of RST has not been widely investigated in water quality analysis. The reducts generated by RST models become very time-consuming as the size of the problem increases. Using multinomial logistics regression (MLR) techniques to provide reducts of RST models, this investigation develops a hybrid Multinomial Logistic Regression and Rough Set Theory (MLRRST) model to analyze relations between degrees of water pollution and environmental factors in Taiwan. Empirical results indicate that the MLRRST model could analyze water qualities efficiently and accurately, and yield decision rules for the staff of water quality management. Thus, the proposed model is a promising and helpful scheme in analyzing water quality.

An Application of Rough Set Analysis toa Psycho-Physiological Study - Assessing the RelationBetween Psychological Scale and Immunological Biomarker

This study aims at an application of rough set theory to illustrate the relationship between human psychological and physiological states. Recent behavioral medicine studies have revealed that various human secretory substances change according to mental states. These substances, the hormones and immune substances, show temporal increase against mental stress. Thus, it is frequently introduced as biomarkers of mental stress. The relationship between these biomarkers and human chronic stresses or daily mental states was also suggested in the previous studies. However the results of these studies were sometimes inconsistent with each other. Some technical reasons were indicated for this discrepancy. Among that, we focused on the analysis technique investigating the relationship between human psychological state, i.e., scores of a psychological scale, and physiological state, i.e., level of the secretory biomarkers. In this paper, we introduced Rough Set analysis method instead of using a conventional linear correlation analysis method. In the experiment, the salivary secretory immunoglobulin A (IgA), which is a major stress biomarker, of 20 male students was assessed before and after a short-term stressful mental workload. Also, 65 items of psychological mood scale was assessed as a psychological index. The result showed that some items strongly related with the change in the IgA, while no significant linear correlation was obtained among them.

Rough set theory with discriminant analysis in analyzing electricity loads

With the ability to deal with both numeric and nominal information, rough set theory (RST), which can express knowledge in a rule-based form, has been one of the most important techniques in data analysis. However, applications of rough set theory for analyzing electricity loads are not widely discussed. Thus, this investigation employs rough set theory to analyze electricity loads. Additionally, to reduce the time generating reducts by rough set theory, linear discriminant analysis (LDA) is used to generate a reduct for rough set model. Therefore, this study designs a hybrid discriminant analysis and rough set model (DARST) to provide decision rules representing relations in an electric load information system. In this investigation, nine condition factors and variations of electricity loads are employed to examine the feasibility of the hybrid model. Experimental results reveal that the proposed model can efficiently and accurately analyze the relation between condition variables and variations of electricity loads. Consequently, the proposed model is a promising alternative for developing an electric load information system and offers decision rules base for the utility management as well as operations staff.

Exploring core competencies for R&D technical professionals

In order to maintain technical competitiveness, companies have an ongoing need to identify and foster core competencies for technical professionals in the area of research and development (R&D). Exploring core competencies is, however, a qualitative problem. The rough set theory (RST), which has recently become well known as a data-mining technique, is a good tool for data reduction in qualitative analysis. Thus, the rough set approach is suitable for dealing with the problem of exploring competencies. This study extends practical applications of RST in mining the critical competencies for R&D technical professionals employed by semiconductor manufacturers in Taiwan. This paper demonstrates a successful reduction of the numerous essential competencies into a more compact set, and arrived at six core competencies which can be regarded as the as indispensable attributes for the design of competency development programs for R&D technical professionals.

Joint Decision Matrix Computation for Rules Extraction in Two-Classes Decision Information System

Abstract Matrix computation for rules extraction of rough set algorithm's inefficiency restricts the application of rough set theory, so the search for efficient algorithms is of great practical significance. Therefore, the joint decision matrix is defined and a new algorithm JDMCRE for rule extraction is presented on the basis of joint decision matrix for two-classes decision information system. By combining both the conditional attribute equivalent matrix and the decisional attribute ones into one matrix, it can considerably reduce the number of comparison in the process of matrix forming, which can effectively increase the efficiency of algorithm. The complexity analysis and experiment results show that the algorithm is much better than existing matrix algorithms.

Rough set uncertainty for robotic systems

The paper provides an overview of the various applications of rough set theory in dealing with uncertainty in modern robotic systems. Rough sets is a relatively young theory that has been gaining p...

Simplifying essential competencies for Taiwan civil servants using the rough set approach

Applying competency models to identify and develop capabilities of civil servants is now a leading strategy for every government. However, an ideal competency model usually contains too many intended competencies, impeding implementation. Recently, some scholars and experts argued that there is a maximum of eight competencies for effective assessment. Hence, how to simplify a set of competencies becomes an important issue. This study is presented as a test case to extend practical applications of rough set theory (RST) in the human resource field of Government. A well-known data mining technique, RST is a relatively new approach to this problem and is good at data reduction in qualitative analysis. Hence, the rough set approach is suitable for dealing with the qualitative problem in simplifying a set of competencies. This paper slimmed a set of competencies using RST, thus helping the Taiwan Government to better understand the perceived competency levels of its civil servants. Using the rough set analysis, this paper successfully reduced the numerous essential competencies into a more compact set, by omitting low-consensus competencies.

Application of Rough Set Theory to High Throughput Screening Data for Rational Selection of Lead Compounds

In the field of drug discovery, high-throughput screening (HTS) is widely used to identify new lead compounds. A considerable number of hit compounds, however, will subsequently be found to have low activities when their inhibitory activities are measured more precisely. Such compounds are called false positives. For a more efficient selection of lead compounds, virtual screening methods with QSAR models have been investigated, but no definitive solutions have been found. In this study, we propose an effective method to identify lead compounds. The proposed method is based on rough set theory (RST), which is a mathematical tool for depicting the uncertainty and vagueness of knowledge. The essential parts of RST are the construction of reducts, which are minimal subsets of variables to distinguish samples, and the extraction of rules using their reducts. By applying RST to the QSAR study of monoamine oxidase (MAO) inhibitors, we extracted several rules for identifying lead compounds. First, 3D-structures of MAO inhibitors were generated uniformly by CORINA, and chemical descriptors were calculated by the Volsurf method. Finally, three unique rules were extracted by using RST. It is found that the each rule is chemically reasonable and compatible with previous studies. Furthermore, the predictive power of RST was also proved by comparison with partial least squares (PLS) and decision tree (DT). These results demonstrate the usefulness of our method.

Application of rough sets theory in knowledge acquisition for the cold extrusion process

Cold extrusion is a process based on intensive knowledge and experience. Many knowledge based processing technologies have been used in this area. In this paper, we have attempted to apply rough sets theory to acquire and analyse the manufacturing knowledge in the cold extrusion process. An over view of the rough sets theory was given, and several reduction algorithms based on information theory and rough sets theory were presented. After defining the cost as decision attribute, some key condition attributes that influence the cost are studied in the cold extrusion process. The detailed procedures that utilise rough sets theory for knowledge acquisitions were introduced, including data preparation, core attributes calculation, attribute reduction, core values calculation and decision rules induction. The implementation of cold extrusion knowledge acquisition using rough sets theory was presented in this paper. The advantages and disadvantages of this method were also discussed in this paper.

Design support systems: A case study of modular design of the set-top box from design knowledge externalization perspective

In this paper, the knowledge externalization of modular product design is introduced, which discovers and shares the design rules to increase the effectiveness/efficiency of the design process. This solution approach involves modeling modular products, formulating their design knowledge, and discovering implicit knowledge into design rules based on the rough set theory. The design rules are generated for improving heat dissipation issues of the set-top box (STB), which is perhaps the most attractive device for the home. A web based knowledge management of product design (KMPD) system is also implemented to provide adapted design rules and share design knowledge in distributed environment. The methodology discussed in the paper departs from the previous developments in design knowledge externalization as well as rough set theory application, thus creating a variable approach for product design.

Rough Sets-Assisted Subfield Optimization for Alternating Current Plasma Display Panel

Plasma display panel technology employs a pulse-number modulation method to generate the gradation of the display. However, this method can create a false contour distortion when moving pictures are reproduced. In this paper, we propose an effective subfield optimization technique which is based on measuring motion picture distortion. To this end, we propose two applications of the measurements. One is optimum selection of the codewords to improve performance, and the other is the application of rough sets theory to subfield optimization, which reduces computational CPU time. Simulation results show that dynamic false contouring can be effectively reduced by this optimization technique, and the complexity can be minimized, using rough sets theory.

Applications of rough set theory to river environment quality evaluation in China

Very often, inexact, uncertain, or vague hydrological data are encountered in water researches. Statistical and fuzzy set theories are applied to deal with hydrological uncertainty problems. In this paper, the basic concept of the rough set theory is introduced and its application to hydrological data is illustrated. The proposed method is applied to water environment and environment quality evaluation of the Hang Jiang River, a major branch of the Yangtze River, in China. Our numerical applications suggest that the rough set theory is a useful tool for analysis of inexact, uncertain, or vague hydrological data.

Fuzzy-Rough Sets Assisted Attribute Selection

Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, attribute selectors preserve the original meaning of the attributes after reduction. This has found application in tasks that involve datasets containing huge numbers of attributes (in the order of tens of thousands) which, for some learning algorithms, might be impossible to process further. Recent examples include text processing and web content classification. AS techniques have also been applied to small and medium-sized datasets in order to locate the most informative attributes for later use. One of the many successful applications of rough set theory has been to this area. The rough set ideology of using only the supplied data and no other information has many benefits in AS, where most other methods require supplementary knowledge. However, the main limitation of rough set-based attribute selection in the literature is the restrictive requirement that all data is discrete. In classical rough set theory, it is not possible to consider real-valued or noisy data. This paper investigates a novel approach based on fuzzy-rough sets, fuzzy rough feature selection (FRFS), that addresses these problems and retains dataset semantics. FRFS is applied to two challenging domains where a feature reducing step is important; namely, web content classification and complex systems monitoring. The utility of this approach is demonstrated and is compared empirically with several dimensionality reducers. In the experimental studies, FRFS is shown to equal or improve classification accuracy when compared to the results from unreduced data. Classifiers that use a lower dimensional set of attributes which are retained by fuzzy-rough reduction outperform those that employ more attributes returned by the existing crisp rough reduction method. In addition, it is shown that FRFS is more powerful than the other AS techniques in the comparative study

Rough approximations on a complete completely distributive lattice with applications to generalized rough sets

The generalizations of rough sets considered with respect to similarity relation, covers and fuzzy relations, are main research topics of rough set theory. However, these generalizations have shown less connection among each other and have not been brought into a unified framework, which has limited the in-depth research and application of rough set theory. In this paper the complete completely distributive (CCD) lattice is selected as the mathematical foundation on which definitions of lower and upper approximations that form the basic concepts of rough set theory are proposed. These definitions result from the concept of cover introduced on a CCD lattice and improve the approximations of the existing crisp generalizations of rough sets with respect to similarity relation and covers. When T-similarity relation is considered, the existing fuzzy rough sets are the special cases of our proposed approximations on a CCD lattice. Thus these generalizations of rough sets are brought into a unified framework, and a wider mathematical foundation for rough set theory is established.

Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches

Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition, and signal processing. This has found successful application in tasks that involve data sets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and Web content classification. One of the many successful applications of rough set theory has been to this feature selection area. This paper reviews those techniques that preserve the underlying semantics of the data, using crisp and fuzzy rough set-based methodologies. Several approaches to feature selection based on rough set theory are experimentally compared. Additionally, a new area in feature selection, feature grouping, is highlighted and a rough set-based feature grouping technique is detailed.

Rule induction in productivity analysis: a rough set approach

This paper describes the application of rough set theory to equipment productivity estimate. Rough set theory offers a novel approach both in generation of rules and statistical classification of the equipment productivity data. The approach is based on upper and lower approximations of a set in terms of positive, negative and boundary regions. The information about the equipment productivity is organized in productivity information table. The table contains data about the object of interest characterized in terms of some attributes. The paper is more of an exploratory research to determine how rough set theory can be applicable to specific construction engineering problems.

Probabilistic approaches to rough sets

Abstract: Probabilistic approaches to rough sets in granulation, approximation and rule induction are reviewed. The Shannon entropy function is used to quantitatively characterize partitions of a universe. Both algebraic and probabilistic rough set approximations are studied. The probabilistic approximations are defined in a decision‐theoretic framework. The problem of rule induction, a major application of rough set theory, is studied in probabilistic and information‐theoretic terms. Two types of rules are analyzed: the local, low order rules, and the global, high order rules.

Mobile clinical support system for pediatric emergencies

This paper describes the process and methodology of designing and developing a mobile support system to triage abdominal pain in the emergency room (ER) of a hospital. Application of rough sets theory and fuzzy measures to data collected at Children's Hospital of Eastern Ontario allowed us to identify the most relevant clinical symptoms and signs while evaluating an abdominal pain patient. This information was used to develop a multilevel clinical algorithm that forms the reasoning module of a clinical support system. We describe a client system called Mobile Emergency Triage (MET) that is installed on Palm handheld and that can be used to triage a child irrespective of the available information. We present MET's functions allowing for the electronic data capture and wireless data transfer.

Classification of meteorological volumetric radar data using rough set methods

This paper reports on a rough set approach to classifying meteorological volumetric radar data used to detect storm events responsible for summer severe weather. The classification of storm cells is a difficult problem due to the complex evolution of storm cells, the high dimensionality of the weather data, and the imprecision and incompleteness of the data. A rough set approach is used to classify different types of meteorological storm events. A considerable of different classification strategies techniques have been considered and compared to determine which approach will best classify the volumetric storm cell data coming from the Radar Decision Support System database of Environment Canada. The criterion for comparison is the accuracy coefficient in the classification over a testing data. The contribution of this paper is a new application of rough set theory in classifying meteorological radar data. ® Elsevier Science B.V. All rights reserved.