Rough Set

Dimensionality refers to number of terms in a web page. While classifying web pages high dimensionality of web pages causes problem. The main objective of reducing dimensionality of web pages is improving the performance of classifier. Processing time and accuracy are two parameters which influence the performance of a classifier. To reduce the processing time, less informative and redundant terms have to be removed from web pages. This research describes hybrid approach for dimensionality reduction in web page classification using a rough set and naïve Bayesian method. Feature selection and dimensionality reduction methods are used for reducing the dimensionality. Information gain method is used as feature selection method. Rough set based Quick Reduct algorithm is used for dimensionality reduction. Naïve Bayesian method is used for classifying web pages to optimal predefined categories. Assignment of web pages to category is based on maximum posterior probability. Words remaining ...

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of

The problem of constructing a tight isothetic outer (or inner) polygon covering an arbitrarily shaped 2D object on a background grid, is addressed in this paper, and a novel algorithm is proposed. Such covers have many applications to image mining, rough sets, computational geometry, and robotics. Designing efficient algorithms for these cover problems was an open problem in the literature.

This paper applies the knowledge discovery process over medical data set using the rough set theory as a data mining technique. The aim is to apply rough set concepts and the reduction algorithm to search for patterns specific/sensitive to thrombosis disease. The mining efforts show that the developed reduction algorithm minimizes the set of original attributes from 60 to 16

This paper presents a review of the current liter- ature on rough set and near set-based approaches to solving various problems in medical imaging such as medical image segmentation, object extraction and image classification. Rough set frameworks hybridized with other computational intelligence technologies that include neural networks, particle swarm op- timization, support vector machines and fuzzy sets are also presented.

Granular Rough Computing is a paradigm within Rough Computing which in turn can be set within the realm of Cognitive Informatics, i.e. machine intelligence tools which emulate cognitive processes in living organisms; its aim is to compute with granules of knowledge that are collective objects formed from individual objects by means of a similarity measure. In this work, we apply the formalism for granule formation proposed by us and studied over last few years which is based on similarity measures called Rough Inclusions. A proper study of rough inclusions has been done within Rough Mereology, a paradigm for approximate reasoning that is rooted in the theory of concepts called Mereology based on the notion of a part instead on the notion of an element like the naive concept theory. We give an outline of granulation theory based on rough inclusions; then, we discuss the most important consequence of similarity among objects in a granule, viz., the hypothesis that granules represent new objects, which preserve the most important features of objects in a granule. This leads to the notion of a granular decision system obtained by means of granulation from an original decision system. The hypothesis that granular decision systems reflect properties of original decision systems to a satisfactory degree, put forth by the author at 2005 and 2006 IEEE GrC conferences, has been tested with very good results. We include here some results of those tests.

Intrusion detection system (IDS) is an important tool for the defense of a network against attacks. It monitors the activities occurring in a computer system or network and analyzes them for recognizing intrusions to protect the computer network. Most of the existing IDSs use all of the 41 features available in the network packet to analyze and look for intrusive

Decision tree, a commonly used classification model, is constructed recursively following a top down approach (from the general concepts to particular examples) by repeatedly splitting the training data set. ID3 is a greedy algorithm that considers one attribute at a time for splitting at a node. In C4.5, all attributes, barring the nominal attributes used at the parent nodes, are retained for further computation. This leads to extra overheads of memory and computational efforts. Rough Set theory (RS) simplifies the search for dominant attributes in the information systems. In this paper, Rough set based Decision Tree (RDT) model combining the RS tools with classical DT capabilities, is proposed to address the issue of computational overheads. The experiments compare the performance of RDT with RS approach and ID3 algorithm. The performance of RDT over RS approach is observed better in accuracy and rule complexity while RDT and ID3 are comparable.

We introduce a hybrid approach to magnetic resonance image segmentation using unsupervised clustering and the rules derived from approximate decision reducts. We utilize the MRI phantoms from the Simulated Brain Database. We run experiments on randomly selected slices from a volumetric set of multi-modal MR images (T1, T2, PD). Segmentation accuracy reaches 96% for the highest resolution images and 89% for the noisiest image volume. We also tested the resultant classifier on real clinical data, which yielded an accuracy of approximately 84%.

Feature selection has become very important research in musical instruments sounds for handling the problem of ‘curse of dimensionality’. In literature, various feature selection techniques have been applied in this domain focusing on Western musical instruments sounds. However, study on feature selection using rough sets of non-Western musical instruments sounds including Malay Traditional musical instruments is inadequate and still needs an intensive research. Thus, in this paper, an alternative feature selection technique using rough set theory based on Maximum Degree of dependency of Attributes (MDA) technique proposed by [8] for Traditional Malay musical instruments sounds is proposed. The modeling process comprises eight phases: data acquisition, sound editing, data representation, feature extraction, data discretization, data cleansing, feature selection using proposed technique and feature validation via classification. The results show that the highest classification accuracy of 99.82% was achieved from the best 17 features with 1-NN classifier.

Recent advances in control engineering suggest that hybrid control strategies, integrating some ideas and paradigms existing in different soft computing techniques, such as fuzzy logic, genetic algorithms, rough set theory, and neural networks, may provide improved control performance in wastewater treatment processes. This paper presents an innovative hybrid control algorithm leading to integrate the distinct aspects of indiscernibility capability of rough set theory and search capability of genetic algorithms with conventional neural-fuzzy controller design. The methodology proposed in this study employs a three-stage analysis that is designed in series for generating a representative state function, searching for a set of multi-objective control strategies, and performing a rough set-based autotuning for the neural-fuzzy logic controller to make it applicable for controlling an industrial wastewater treatment process. Research findings in the case study clearly indicate that the ...

This paper presents an approach in analyzing personality types, temperament and team diversity to determine software engineering (SE) teams performance. The benefits of understanding personality types and its relationships amongst team members are crucial for project success. Rough set analysis was used to analyze Myers-Briggs Type Indicator (MBTI) personality types, Keirsey temperament, team diversity, and team performance. The result shows positive relationships between these attributes.

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