Rough Set-based Data Analysis Research Articles

Assistant Tools for Medical Diagnostics through Rough Set-Based Data Analysis

Assistant Tools for Medical Diagnostics through Rough Set-Based Data Analysis

Breast cancer classification based on improved rough set theory feature selection

Breast cancer is one of the leading causes of death among the women. Mammogram analysis is the most effective method that helps in the early detection of breast cancer. In this paper we have made an attempt to classify the breast tissue based on Statistical features of a mammogram which extracted using simple image processing techniques with rough set theory. The proposed scheme uses texture models to capture the mammographic appearance within the breast. The statistical features extracted are the mean, standard deviation, smoothness, third moment, uniformity and entropy which signify the important texture features of breast tissue. Based on the values of these features of a digital mammogram, we have made an attempt to classify the breast tissue in to three basic categories normal, benign, and malignant given in the data base (mini-MIAS database). This categorization would help a radiologist to detect a normal breast from a cancer affected breast. Rough set theory can be regarded as a new mathematical tool for imperfect data analysis. Rough set based data analysis starts from a data table called a decision table. Each row of a decision table induces a decision rule, which specifies decision (action, results, outcome, etc.). We can know important data rules by using core and reduct which elimination of duplicate rows and elimination of superfluous values of attributes.

Incremental updating of rough approximations in interval-valued information systems under attribute generalization

Interval-valued Information System (IvIS) is a generalized model of single-valued information system, in which the attribute values of objects are all interval values instead of single values. The attribute set in IvIS is not static but rather dynamically changing over time with the collection of new information, which results in the continuous updating of rough approximations for rough set-based data analysis. In this paper, on the basis of the similarity-based rough set model in IvIS, we develop incremental approaches for updating rough approximations in IvIS under attribute generalization, which refers to the dynamic changing of attributes. Firstly, increment relationships between the original rough approximations and the updated ones when adding or deleting an attribute set are analyzed, respectively. And the incremental mechanisms for updating rough approximations in IvIS are introduced, which carry out the computation using the previous results from the original data set along with new results. Then, the corresponding incremental algorithms are designed based on the proposed mechanisms. Finally, comparative experiments on data sets from UCI as well as artificial data sets are conducted, respectively. Experimental results show that the proposed incremental algorithms can effectively reduce the running time for the computation of rough approximations in comparison with the static algorithm.

Rough Set-Based Data Analysis in Converter Fault Diagnosis for Hybrid Power Substation

The fault in power system cannot be completely avoided. In this paper, we developed a method to resolve fault localization problems in power system. Though the data acquisition process has been highly automated, the process of assimilating and analyzing data still lags behind. Raw data must be transformed into knowledge in order to help users decide how to respond to the event and implement the necessary actions. A promising technique for substation event analysis using rough set theory is described in this paper. It interprets the data and outputs meaningful and concise information, which improves the performance of a data analysis system and help with the knowledge acquisition process. A substation model was developed to generate various fault scenarios for our case studies to evaluate the performance of the rough set algorithm. The results show that it works well and efficiently with the overwhelming data.

Stable rule extraction and decision making in rough non-deterministic information analysis

Rough Non-deterministic Information Analysis (RNIA) is a rough set-based data analysis framework for Non-deterministic Information Systems (NISs). RNIA-related algorithms and software tools developed so far for rule generation provide good characteri

A relational perspective of attribute reduction in rough set-based data analysis

Attribute reduction is very important in rough set-based data analysis (RSDA) because it can be used to simplify the induced decision rules without reducing the classification accuracy. The notion of reduct plays a key role in rough set-based attribute reduction. In rough set theory, a reduct is generally defined as a minimal subset of attributes that can classify the same domain of objects as unambiguously as the original set of attributes. Nevertheless, from a relational perspective, RSDA relies on a kind of dependency principle. That is, the relationship between the class labels of a pair of objects depends on component-wise comparison of their condition attributes. The larger the number of condition attributes compared, the greater the probability that the dependency will hold. Thus, elimination of condition attributes may cause more object pairs to violate the dependency principle. Based on this observation, a reduct can be defined alternatively as a minimal subset of attributes that does not increase the number of objects violating the dependency principle. While the alternative definition coincides with the original one in ordinary RSDA, it is more easily generalized to cases of fuzzy RSDA and relational data analysis.

Rough sets and intelligent data analysis

Rough set based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm can be associated. It is shown that every decision algorithm reveals some well-known probabilistic properties, in particular it satisfies the total probability theorem and the Bayes’ theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.

Rough sets, decision algorithms and Bayes' theorem

Rough set-based data analysis starts from a data table, called an information system. The information system contains data about objects of interest characterized in terms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a decision table. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. With every decision table a set of decision rules, called a decision algorithm, can be associated. It is shown that every decision algorithm reveals some well-known probabilistic properties, in particular it satisfies the total probability theorem and Bayes' theorem. These properties give a new method of drawing conclusions from data, without referring to prior and posterior probabilities, inherently associated with Bayesian reasoning.