Rough Set Data Analysis Research Articles

An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm.

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

An Intelligent Mining Model for Medical Diagnosis of Heart Disease Based on Rough Set Data Analysis

An Intelligent Mining Model for Medical Diagnosis of Heart Disease Based on Rough Set Data Analysis

Construction of network security job service model based on rough set data analysis algorithm

Traditional network security job service model and single security technology cannot keep up with the changes of complex network structure and different intrusion measures. Network security job service model relying on rough dataset analysis algorithm has many advantages, such as low management cost, high flexibility and wide applicability. Rough dataset analysis algorithm can not only collect data, but also process data, but overcome the shortcomings of traditional network security job service model. It will improve response speed and reduce network burden. This paper introduces the construction of network security job service model, which based on rough dataset analysis algorithm into a new network security framework.

An application of the logic of explanatory power in rough set analysis: implications for the classification of decision rules

This paper uses the logic of explanatory power to address the question of uncertain decision rule classification and interpretation in rough set data analysis. A set theoretic configuration of the measure of explanatory power is introduced. The usefulness of the measure is then examined in the context of two datasets - one related to car evaluation and the other related to the provision of extra educational supports. It is found that the explanatory power measure has some interesting properties that enhance the informativeness and interpretation of non-deterministic decision rules. The result of the numerical analysis shows that the explanatory power index is unique. The index can also facilitate the establishment of an objective threshold that determines whether the explanatory relevance of the premise in a given decision rule is positive, negative, or neutral.

An application of the logic of explanatory power in rough set analysis: implications for the classification of decision rules

This paper uses the logic of explanatory power to address the question of uncertain decision rule classification and interpretation in rough set data analysis. A set theoretic configuration of the measure of explanatory power is introduced. The usefulness of the measure is then examined in the context of two datasets - one related to car evaluation and the other related to the provision of extra educational supports. It is found that the explanatory power measure has some interesting properties that enhance the informativeness and interpretation of non-deterministic decision rules. The result of the numerical analysis shows that the explanatory power index is unique. The index can also facilitate the establishment of an objective threshold that determines whether the explanatory relevance of the premise in a given decision rule is positive, negative, or neutral.

Comparative Overview of Rough Set Toolkit Systems for Data Analysis

Inconsistency, lacking values of attributes or parameters, as well as discrepancies between records caused by insufficient precision cannot always be managed in the initial phases of knowledge discovery,i.e., data preparation and refinement. The theory of rough sets aims to overcome problems that are caused by uncertainty and lack of precision within the gathered data sets. This approach is a useful tool that operates on a formal model using relational algebra, elementary operations on finite sets and first-order logic. In this paper, we present an analysis of existing rough set tools, namely: Rough Set Exploration System, Rough Sets Data Explorer, Rough Set Data Analysis Framework, Waikato Environment for Knowledge Analysis and Rough Set Toolkit for Analysis of Data. Our comparison is performed only theoretically and covers the available algorithms, preparation of input data, licensing, as well as installation requirements.

Statistical Inference in Rough Set Theory Based on Kolmogorov–Smirnov Goodness-of-Fit Test

Dependence degree (DD) and importance degree (ID) of patterns are crucial for pattern appraisement and model reconstruction. In rough set data analysis (RSDA), DD and ID lack robustness because the lower approximation set is terribly unstable under the indiscernibility relation perturbation. Statistical inference is a good way to deal with this instability. However, the fixed-value hypothesis testing and interval estimation of DD and ID were only discussed by the $\chi ^2$ test, which merely suits for two contingency tables (CT) with the same number of elements, and their nonzero elements must exist at the same positions. These requirements are too strict for practical applications. In this paper, the Kolmogorov–Smirnov (K–S) goodness-of-fit test is introduced to generalize the statistical inferences of DD and ID. As the bridge between data and corresponding measures, CT lies at the core of RSDA. By transforming CT to a random sample of a hypothesized random variable, the elementary algorithm for the goodness-of-fit test of contingency tables and the advanced algorithm for the goodness-of-fit test of contingency tables are proposed based on K–S statistic to implement goodness-of-fit tests of CTs. Better than $\chi ^2$ test, all CTs, even with different numbers and different positions of nonzero elements, are permitted. Subsequently, by generating a CT with expected DD value, the fixed-value hypothesis of DD is transformed to the goodness-of fit test between the original and expected CTs. Three algorithms, i.e., hypothesis test of dependence degree based on the K–S test, region estimation of the dependence degree, and significance test and region estimation of importance degree of attribute set, are proposed as fixed-value hypothesis tests and region estimations of DD and ID. These algorithms can be used to verify the importance of attributes and choose the attribute subset with the highest likelihood of maintaining the original discrimination ability. Experiments verify that the discrimination ability, disturbance tolerance ability, and stability under varying discretization strategies of DD and ID are significantly enhanced by the proposed algorithms.

Statistical Inference of Rough Set Dependence and Importance Analysis

Statistical inference about dependence degree (DD) and importance degree (ID) of variables in an information system is crucial for variables appraisement and model reconstruction. However, in rough set data analysis (RSDA), the literature is restricted to validate independence or test whether the degree is significantly big, while the fixed value test and interval estimation for related measurements have been ignored. Because these important issues have not been addressed, we cannot determine whether the data support expert opinions and compare the features in depth. To enhance the integrity of statistical inference for DD and ID in an RSDA, fixed value tests and interval estimations of DD and ID are presented in this paper. With multinomial distribution as the carrier for statistical information in the databases, the fixed value test of DD is successfully transformed into a restricted estimation of multinomial distribution and a goodness-of-fit test for distributions. The fixed value test and interval estimation algorithms for DD and ID are then presented in detail and illustrated with examples. Explicit expressions for the DD and ID interval estimation, DD confidence curves, and the limit theory for DD and ID are shown. Furthermore, the effectiveness and discrimination of the proposed algorithms are validated using the Car evaluation, Tic-Tac-Toe endgame, and Fisher's Iris databases.

RSDA-PID Control on Synchro-Motion System with Four Cylinders

Considering that synchro-motion performance of four cylinders was influenced by flow allocation of one to more pipes, a nonlinear dynamics model of electro-hydraulic servo system with four cylinders was presented. Then a RSDA-PID synchro controller for electro-hydraulic servo system with four cylinders was provided with PID control, fuzzy control and RSDA(rough set data analysis) theory. This controller based on the equivalent synchro-motion control strategy. The simulation and factual applications show that the proposed control approach has better tracking and synchronization performance than that of conventional control strategy.

Research on Intelligent Vehicle Steering System via Improved Fuzzy-PID Control Method Based on RSDA

In order to improve the control accuracy and response speed of the intelligent vehicle steering system, a DC-motor controlled steering system using an IPC (Industrial personal computer) is designed. The hardware of the system is developed by analyzing the dynamic steering model. A new control method of Fuzzy-PID controller with RSDA (Rough Set Data Analysis) is proposed. Experimental results using the proposed control strategy show better performance and robust properties than the ordinary Fuzzy-PID.

Knowledge Acquisition of Spindle Bearings Fault Based on Rough Sets

An identification method of spindle bearing fault based on rough sets theory is proposed in the article. By collecting bearing’s typical fault signal and using signal information processing techniques, vibration fault data is obtained. Then, equidistant clustering analysis method is introduced into discretization of experimental data of continuous attributes. In this way, vibration fault data table meets the requirement of rough sets data analysis. Besides, attribute importance algorithm is used in order to realize the reduction of condition attribute in the decision table. Thus, fault information which hidden in huge signal data is extracted. Therefore, simple and clear fault pattern rules are acquired. The result indicates that the method can realize fault pattern identification of spindle’s bearings and it is of great application value in practical fault pattern identification.

EMBEDDEDNESS OF ENTREPRENEURS IN RURAL AREAS: A COMPARATIVE ROUGH SET DATA ANALYSIS

ABSTRACTThis study aims to identify the critical factor(s) that determine the embeddedness level (EL) of rural entrepreneurs. In order to achieve this aim, existing applied studies on the embeddedness of entrepreneurs undertaken in different rural areas were systematically collected to create a database in order to provide the material for a systematic comparative analysis. This was done in order to highlight common and contrasting findings from a set of selected studies for different ELs. As many results of these studies were largely qualitative in nature and only partially comparable, a specific tool for analysing categorical data based on artificial intelligence methods, namely, rough set data analysis (RSDA), was employed. This experimental study is the first RSDA approach that compares the results of several rural case studies and infers general induction rules for the different ELs. The results of our analysis show that using and benefiting from local resources are the key factors that explain how entrepreneurs become embedded in rural areas.

Data mining in topology education: Rough set data analysis

This study explores rough sets, which provide mathematical description for uncertain concepts that can not be defined clearly by traditional logic. The study further examines data mining, which helps to discover meaningful information from large data sets by incorporating it with rough set theory. An investigation in topology instruction was undertaken to exemplify the usability of rough set theory in qualitative data analysis. The study provides an example of educational application of data mining in a topology course. Key words: Artificial Intelligence, rough sets, educational data mining, topology

Application of Rough Set Analysis in Species and Caste Discrimination of Bumblebees (Hymenoptera: Apidae: Bombus) Based on Antennal Sensilla

Abstract Using scanning electron microscope, eight types of sensilla were found on the antennae of all castes in eight Bombus species (Hymenoptera: Apidae) collected in Jilin Province, China. The numbers of various types of antennal sensilla were analyzed by rough set data analysis method to determine which sensillum type might be useful in species and caste discrimination. The results showed that the number of sensilla basiconica A, B (s. t. a, s. t. b), sensilla trichodea A, B, C/ D (s. t. a, s. t. b, s. t. c/ d) and sensilla placodea (s. p) can be used in species discrimination, whereas only types s. t. a, s. t. b, and s. t. b are relevant in caste discrimination. Other types of sensilla had a lower taxonomic value for species and caste discrimination. This study provides a mathematic reference for the function research of antennal sensilla of Bombus determined by single-cell recording at both peripheral and antennal lobe (central nervous system) levels.

An alternative evaluation method for Likert type attitude scales: Rough set data analysis

Likert scales are the most prevalent attitude scale. However, the fact that different items could be added up to produce same attitude values decreases the validity of the Likert scale results. Rough set data analysis could perhaps be conducted to overcome related limitations. Thus, this study seeks to investigate the employability of the rough set approach for the interpretation of Likert type attitude scales. Data was collected through a scale developed by using four sub-dimensions of the Fennema-Sherman mathematics attitude scale. In order to carry out the analysis, students were distributed to three groups of high, moderate and low in terms of their attitude to mathematics. Data was then converted to appropriate information tables for rough set analysis and lower and upper approximation sets were specified for these three groups. Accordingly, the potential membership of some students, who already belong to certain groups, to other groups was determined via rough sets. The mathematical value of to what extent each sub-dimension or any group can explain the total score was calculated. Through reduction of attributes, anxiety and usefulness sub-dimensions were found to be the indispensable sub-dimensions. The findings indicate that the rough set approximate can be used for the analysis of attitude scales. Key words: Attitude scale, rough sets, data mining, approximations, data analysis.

Smart case-based indexing in worsted roving process: Combination of rough set and case-based reasoning

Rough set data analysis (RSDA) has been primarily studied in order to obtain knowledge rules. Taking one group of data as an example, according to the simplified attributes and extractive rules a case was established; using case-based reasoning (CBR), another case was established based on the parameters influencing on the roving quality. In addition, RSDA was combined with CBR to build the case library. This allowed unimportant parameters to be removed and the case library to be simplified; in turn allowing easier, more efficient searching of attributes from the simplified case library. The union of Rule-Based Reasoning (RBR) and CBR means that complicated calculations around similar cases and the associated error can be avoided. Using RSDA one is able to reveal characteristic attributes and deduce knowledge rules associated with the model/problem in order to build a case library directly from historical data. So using the above procedure, allows machine settings to be defined and the prediction and control of the end-product quality to be easier.

Financial time-series analysis with rough sets

We investigate the use of the rough set model for financial time-series data analysis and forecasting. The rough set model is an emerging technique for dealing with vagueness and uncertainty in data. It has many advantages over other techniques, such as fuzzy sets and neural networks, including attribute reduction and variable partitioning of data. These characteristics can be very useful for improving the quality of results from data analysis. We demonstrate a rough set data analysis model for the discovery of decision rules from time series data for example, the New Zealand stock exchanges. Rules are generated through reducts and can be used for future prediction. A unique ranking system for the decision rules based both on strength of the rule and stability of the rule is used in this study. The ranking system gives the user confidence regarding their market decisions. Our experiment results indicate that the forecasting of future stock index values using rough sets obtains decision ruleswith high accuracy and coverage.

Embeddedness of Entrepreneurs in Rural Areas: A Comparative Rough Set Data Analysis

This study aims to identify the critical factor(s) that determine the embeddedness level (EL) of rural entrepreneurs. In order to achieve this aim, existing applied studies on the embeddedness of entrepreneurs undertaken in different rural areas were systematically collected to create a database in order to provide the material for a systematic comparative analysis. This was done in order to highlight common and contrasting findings from a set of selected studies for different ELs. As many results of these studies were largely qualitative in nature and only partially comparable, a specific tool for analysing categorical data based on artificial intelligence methods, viz. rough set data analysis (RSDA), was employed. This experimental study is the first RSDA approach that compares the results of several rural case studies and infers general induction rules for the different ELs. The results of our analysis show that using and benefiting from local resources are the key factors that explain how entrepreneurs become embedded in rural areas.

Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the 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. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

ROUGH-FUZZY PID CONTROL AND ITS APPLICATION TO AUTO-SHAPING MACHINE

An approach of rough-fuzzy control, which uses rough set data analysis to upgrade fuzzy PID control, is presented for the electro-hydraulic servo position system in pipe bundle shaping process. The input to the model is mapped into the output subspace by using rules acquired from RSDA, then the output of the system is approximated in the subspace. On the basis of RSDA, modeling and analysis of actual pipe bundle shaping control process is provided. Compared with conventional fuzzy PID control, this method takes on better dynamic running performance with simple control arithmetic.