Rough Classification Research Articles

Probabilistic Rough Classification in Information Systems with Fuzzy Decision Attributes

Based on Pawlaks two way approximations on Rough Sets and using thresholds G.Ganesan et al in 2004 proposed a method of rough indexing an information system which ha s fuzzy decision attributes. The limitation of Pawlaks approximation is that it does not quantify the level of importance of the basic granules. Recently, Y.Y.Yao discussed Probabilistic Rough Set Model, which specified how basic granules could be quantif ied appropriately. In this paper, it is proposed to extend the work of G.Ganesan et al, taking into consideration the basic granule quantification mechanism of Probabilistic Set Model, thus generating more accurate rough indices for information systems wi th fuzzy decision attributes.

Shape Description and Recognition Method Inspired by the Primary Visual Cortex

The shape or contour of an object is usually stable and persistent, so it is a good basis for invariant recognition. Before this can be accomplished, two problems must be handled. The first is obtaining clean edges, and the other is organizing those edges into a structured form so they can be manipulated easily. Simple cells in the primary visual cortex are specialized for orientation detection. This neural mechanism can be stimulated using an array model. This model can produce a fairly clean set of lines, all in the form of vectors instead of pixels. These multiple orientation layers are then disintegrated from the original array, and a hierarchical partition tree can be created to re-organize them. Based on the similarity of the trees, a rough classification of objects can be realized. To enable maximum recognition, a moment-based measurement method was designed to describe the layout of active simple cells in each layer in detail. Then, a decision tree was produced using samples re-described using Hu’s moment invariants. This system takes into account more geometric information during the recognition process. The experimental results suggest that the representation efficiency enabled by simple cells, and their neural mechanism and the application of a multi-layered representation schema can simplify the algorithm. This further demonstrates the crucial role of simple cells in the visual processing path and shows that they can facilitate subsequent shape processing.

Study on Increasing the Accuracy of Classification Based on Ant Colony algorithm

Abstract. The application for GIS advances the ability of data analysis on remote sensing image. The classification and distill of remote sensing image is the primary information source for GIS in LUCC application. How to increase the accuracy of classification is an important content of remote sensing research. Adding features and researching new classification methods are the ways to improve accuracy of classification. Ant colony algorithm based on mode framework defined, agents of the algorithms in nature-inspired computation field can show a kind of uniform intelligent computation mode. It is applied in remote sensing image classification is a new method of preliminary swarm intelligence. Studying the applicability of ant colony algorithm based on more features and exploring the advantages and performance of ant colony algorithm are provided with very important significance. The study takes the outskirts of Fuzhou with complicated land use in Fujian Province as study area. The multi-source database which contains the integration of spectral information (TM1-5, TM7, NDVI, NDBI) and topography characters (DEM, Slope, Aspect) and textural information (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, Correlation) were built. Classification rules based different characters are discovered from the samples through ant colony algorithm and the classification test is performed based on these rules. At the same time, we compare with traditional maximum likelihood method, C4.5 algorithm and rough sets classifications for checking over the accuracies. The study showed that the accuracy of classification based on the ant colony algorithm is higher than other methods. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. The causes of LUCC have been analysed and some suggestions to the development of this region were proposed.

Using head poses to control a virtual robot walking in a virtualmaze

This paper develops a game system based on head pose identification techniques to control a virtual robot walking in a virtual maze environment built with Microsoft Robotics Developer Studio. Adaboost face classifiers are used to detect a face in the image and give a rough head pose classification. Two approaches are proposed to further determine more specific head poses. The first approach adopts PCA with nose template matching method, and the second one firstly uses a nose classifier to detect the nose in a detected face region and then directly utilizes the nose’s relative position information to achieve head pose classification. A method to select the best nose area is proposed to solve the problem that multi-nose areas may be detected in one face region. Experiment results show that the two approaches have comparable time consumed, but the second approach provides more correct identification results.

A Current Update on the Rule of Alternative and Complementary Medicine in the Treatment of Liver Diseases

There is a vast body of knowledge which is ever-increasing about the treatment of liver disease with alternative and complementary medicine for which hundreds of thousands of literatures have been documented. Liver disease is a general term. This term covers all the potential problems that cause the liver to fail to perform its specified operations. Liver disease has a variety of presentations and causes a great public health problem worldwide which threatens the wellness of billions of people. Incidences of many types of liver disease are currently rising. Although there is still a debate about the entity of alternative and complementary medicine, it is now widely used and it is improving. And it covers the shortages and compensates for the weaknesses of conventional methods in the treatment of liver diseases. Alternative and complementary medicine for liver diseases provides benefits by regulating immunity, controlling disease progression, improving quality of life, and prolonging survival. This paper reviews the increasing interest and growing research into alternative and complementary medicine for liver diseases, with a look at the rough classification, principle of management, evidence-based applications, and issues for prescription and perspectives.

Mathematical Models of Abstract Systems: Knowing abstract geometric forms

Scientists use models to know the world. It is usually assumed that mathematicians doing pure mathematics do not. Mathe- maticians doing pure mathematics prove theorems about mathematical entities like sets, numbers, geometric figures, spaces, etc., they compute various functions and solve equations. In this paper, I want to exhibit models build by mathematicians to study the fundamental components of spaces and, more generally, of mathematical forms. I focus on one area of mathematics where models occupy a central role, namely homotopy theory. I argue that mathematicians introduce genuine models and I offer a rough classification of these models.

On birational involutions of

We study elements of order two in the birational automorphism groups of rationally connected three-dimensional algebraic varieties such that there exists a non-uniruled divisorial component of the -fixed point locus. Using the equivariant minimal model program, we give a rough classification of such elements.

Betti and Tachibana numbers

We present a rough classification of differential forms on a Riemannian manifold, we consider definitions and properties of conformal Killing forms on a compact Riemannian manifold and define Tachibana numbers as an analog of the well known Betti numbers. We state the conditions that characterize these numbers. In the last section we show connections between the Betti and Tachibana numbers.

Mod p representations ofSL2(Qp)

In Barthel and Livne (1994) [3] , the authors give a rough classification of mod p representations of GL 2 ( F ) where F is a finite extension of Q p . In Breuil (2003) [4] , the author gives a complete classification of mod p representations of GL 2 ( Q p ) . In this paper, using the classification in those two papers, we classify mod p representations of SL 2 ( Q p ) . We also define conductors and newforms for these representations and compute the conductors and the dimensions of spaces of newforms.

Method of Disease Diagnosing Based on SVM and Rough Set

To solve the problem that large training samples and slow speed in diagnosing based on support vector classifier, a hybrid classification algorithm applying attribute reduction of rough set and classification principles of SVM to diagnose diseases was proposed. RS was used to preprocess the attributes on condition that no effective information was lost. Redundant attributes and conflicting objects from decision table was deleted. And the dimension and complexity in the process of SVM classification was reduced. Then it classifies and forecasts objects by SVM classifier, so as to achieve the diagnosis for case. Experiments show that it can improve the rate of diagnosis under the reasonable reducing accurate rating after RS reducing information, and can improve the speed of diagnosing diseases when SVM dealing with much disease information.

Detection and classification of surface roughness in an Arctic marginal sea ice zone

Sea ice dynamic and thermodynamic processes are important and highly variable elements of the marginal ice zone (MIZ). This study examines the detection and classification of statistically separable sea ice classes in the MIZ through a range of temporal and spatial scales. A helicopter-based laser system was used to obtain large-scale and a ship-based laser profiler to identify small-scale roughness types, respectively. The analysis of variance of surface height data from helicopter- and ship-based laser systems, active microwave (AMW) C-band backscattering data and passive microwave (PMW) (37 and 89 GHz) brightness temperature data reveal different classes that statistically differ from one another. We found significant statistical difference in variances in AMW data with six classes that differ in VV polarization, three classes in VH polarization, and five classes in HH polarization in the MIZ (e.g. snow-covered first-year ice, ice rubble, pancake ice, frost flowers, melt pond, flooded ice, and ice edge) of southeastern Beaufort Sea. The PMW emission was not as effective at discrimination, yielding only one statistically separable class. The results can potentially be extended to satellite-based investigations of the MIZ at regional scales. Copyright © 2012 John Wiley & Sons, Ltd.

Two Proximal Skin Electrodes — A Respiration Rate Body Sensor

We propose a new body sensor for extracting the respiration rate based on the amplitude changes in the body surface potential differences between two proximal body electrodes. The sensor could be designed as a plaster-like reusable unit that can be easily fixed onto the surface of the body. It could be equipped either with a sufficiently large memory for storing the measured data or with a low-power radio system that can transmit the measured data to a gateway for further processing. We explore the influence of the sensor's position on the quality of the extracted results using multi-channel ECG measurements and considering all the pairs of two neighboring electrodes as potential respiration-rate sensors. The analysis of the clinical measurements, which also include reference thermistor-based respiration signals, shows that the proposed approach is a viable option for monitoring the respiration frequency and for a rough classification of breathing types. The obtained results were evaluated on a wireless prototype of a respiration body sensor. We indicate the best positions for the respiration body sensor and prove that a single sensor for body surface potential difference on proximal skin electrodes can be used for combined measurements of respiratory and cardiac activities.

모델 그래프를 이용한 빠른 필기 인식 방법

Rough classification methods are used to improving the recognition speed in many character recognition problems. In this case, some irreversible result can occur by an error in rough classification. Methods for duplicating each model in several classes are used in order to reduce this risk. But the errors by rough classfication can not be completely ruled out by these methods. In this paper, an recognition method is proposed to increase speed that matches models selectively without any increase in error. This method constructs a model graph using similarity between models. Then a search process begins from a particular point in the model graph. In this process, matching of unnecessary models are reduced that are not similar to the input pattern. In this paper, the proposed method is applied to the recognition problem of handwriting numbers and upper/lower cases of English alphabets. In the experiments, the proposed method was compared with the basic method that matches all models with input pattern. As a result, the same recognition rate, which has shown as the basic method, was obtained by controlling the out-degree of the model graph and the number of maintaining candidates during the search process thereby being increased the recognition speed to 2.45 times.

Local rule distributions, language complexity and non-uniform cellular automata

This paper investigates a variant of cellular automata, namely ν-CA. Indeed, ν-CA are cellular automata which can have different local rules at each site of their lattice. The assignment of local rules to sites of the lattice completely characterizes ν-CA. In this paper, sets of assignments sharing some interesting properties are associated with languages of bi-infinite words. The complexity classes of these languages are investigated providing an initial rough classification of ν-CA.

How specific is source memory for faces of cheaters? Evidence for categorical emotional tagging.

Two experiments designed to examine the specificity of emotional source memory are reported. In the encoding phase, participants saw faces along with emotional context information, that is, descriptions of cheating, trustworthy, or irrelevant behavior. In the test phase, participants were required to complete a source classification test and a cued recall test. In both experiments, the source memory advantage for faces characterized by negative context information (cheating) was replicated. Extending previous research, a multinomial source-monitoring model was applied to distinguish between specific source memory for individual behavior descriptions and partial source memory in the sense of only a rough classification of the behavior as belonging to a particular emotional category--cheating, trustworthy, or neither of these. The results indicate that the source memory advantage for the emotional context information is not always accompanied by enhanced recollection of the specific details of the learning episode and might rather reflect unspecific memory for categorical emotional information.

Comparison of Different Rule Calculation Method forRough Set Theory

In the paper, information table for network traffic data is discretized by supervised learning using WEKA and from this discretized data, three different algorithms for calculation of rules are applied, genetic algorithm, covering algorithm and LEM2 algorithm. Rough set classification through three different table rule set are done and accuracies are observed. It has been concluded that out of these three methods of rule calculation, classification through table rule set using covering algorithm yields best accuracy. In fourth table of classification, rules have been calculated through reduct generation but in that case, rough set classification produces same total accuracy of classification through covering algorithm. RSES software is used for rule set calculation, reduct generation and rough set classification.

Evaluation of Rough Set Theory Based Network TrafficData Classifier Using Different Discretization Method

In information security, intrusion detection is a challenging task for which designing of an efficient classifier is most important. In the paper, network traffic data is classified using rough set theory where discretization of data is a necessary preprocessing step. Different discretization methods are available and selection of one has great impact on classification accuracy, time complexity and system adaptability. Three discretization methods are applied on continuous KDD network data namely, rough set exploration system (RSES), supervised and unsupervised discretization methods to evaluate the classifier accuracy. It has been observed that supervised discretization yields best accuracy for rough set classification and provides system adaptability.

Intelligent Recognition of Extension Group Decision-making under Uncertainty

Regarding to the uncertain extension group decision making, this paper combine the rough set and extension set to utilize both of their advantages. Based on extension transformation, we discuss the attribute-dimension reduction of uncertain extension group decision making and rule extraction. In addition, correlation based core data range and extendable data range are also studied and analyzed to gain multiple-strategic rough classification of decision objects and dynamic recognition of system decision making problems under uncertain condition. The results showed that this method outperforms others in terms of data classification, filtering, rule extraction and strategy recognition, especially when sample size are small, uncertain and descriptive.

Recognizing basic structures from mobile laser scanning data for road inventory studies

Road safety inspection is currently carried out by time-consuming visual inspection. The latest mobile mapping systems provide an efficient technique for acquiring very dense point clouds along road corridors, so that automated procedures for recognizing and extracting structures can be developed. This paper presents a framework for structure recognition from mobile laser scanned point clouds. It starts with an initial rough classification into three larger categories: ground surface, objects on ground, and objects off ground. Based on a collection of characteristics of point cloud segments like size, shape, orientation and topological relationships, the objects on ground are assigned to more detailed classes such as traffic signs, trees, building walls and barriers. Two mobile laser scanning data sets acquired by different systems are tested with the recognition methods. Performance analyses of the test results are provided to demonstrate the applicability and limits of the methods. While poles are recognized for up to 86%, classification into further categories requires further work and integration with imagery.

A Rough Set-based gas turbine fault classification approach using enhanced fault signatures

Gas turbine engine health management has become more and more important because of its ability to optimize the total gas turbine operation. Gas path fault classification is one of the most important techniques in gas turbine engine health management. In this article, a Rough Set-based gas turbine fault classification approach is introduced to enhance gas turbine engine health management by taking its advantages in selecting appropriate measurements for fault classification and dealing with uncertainties caused by measurement noise. In the approach, a Rough Set-based knowledge discovery tool is used to find the knowledge hidden in fault samples, and transfer the knowledge into rules representing the logical relationship between the faults and the fault signatures. Such rules can then be used by the Rough Set diagnostic approach to classify faults. Enhanced fault signatures, represented by the measurement deviations and their ranking pattern in terms of their magnitude, are used to make the diagnostic approach more effective. The Rough Set-based diagnostic approach was applied to a model two-spool turbofan gas turbine engine for the classification of single- and dual-component faults. The results show that such Rough Set-based diagnostic approach is able to classify complex-component faults accurately in the presence of measurement noise.