Improving circuit miniaturization and its efficiency using Rough Set Theory

High-speed, accuracy, meticulousness and quick response are notion of the vital necessities for modern digital world. An efficient electronic circuit unswervingly affects the maneuver of the whole system. Different tools are required to unravel different types of engineering tribulations. Improving the efficiency, accuracy and low power consumption in an electronic circuit is always been a bottle neck problem. So the need of circuit miniaturization is always there. It saves a lot of time and power that is wasted in switching of gates, the wiring-crises is reduced, cross-sectional area of chip is reduced, the number of transistors that can implemented in chip is multiplied many folds. Therefore to trounce with this problem we have proposed an Artificial intelligence (AI) based approach that make use of Rough Set Theory for its implementation. Theory of rough set has been proposed by Z Pawlak in the year 1982. Rough set theory is a new mathematical tool which deals with uncertainty and vagueness. Decisions can be generated using rough set theory by reducing the unwanted and superfluous data. We have condensed the number of gates without upsetting the productivity of the given circuit. This paper proposes an approach with the help of rough set theory which basically lessens the number of gates in the circuit, based on decision rules.Comment: The International Conference on Machine Intelligence Research and Advancement,ICMIRA-201

Geometric lattice structure of covering and its application to attribute reduction through matroids

The reduction of covering decision systems is an important problem in data mining, and covering-based rough sets serve as an efficient technique to process the problem. Geometric lattices have been widely used in many fields, especially greedy algorithm design which plays an important role in the reduction problems. Therefore, it is meaningful to combine coverings with geometric lattices to solve the optimization problems. In this paper, we obtain geometric lattices from coverings through matroids and then apply them to the issue of attribute reduction. First, a geometric lattice structure of a covering is constructed through transversal matroids. Then its atoms are studied and used to describe the lattice. Second, considering that all the closed sets of a finite matroid form a geometric lattice, we propose a dependence space through matroids and study the attribute reduction issues of the space, which realizes the application of geometric lattices to attribute reduction. Furthermore, a special type of information system is taken as an example to illustrate the application. In a word, this work points out an interesting view, namely, geometric lattice to study the attribute reduction issues of information systems

The probability of default in internal ratings based (IRB) models in Basel II: an application of the rough sets methodology

El nuevo Acuerdo de Capital de junio de 2004 (Basilea II) da cabida e incentiva la implantación de modelos propios para la medición de los riesgos financieros en las entidades de crédito. En el trabajo que presentamos nos centramos en los modelos internos para la valoración del riesgo de crédito (IRB) y concretamente en la aproximación a uno de sus componentes: la probabilidad de impago (PD). Los métodos tradicionales usados para la modelización del riesgo de crédito, como son el análisis discriminante y los modelos logit y probit, parten de una serie de restricciones estadísticas. La metodología rough sets se presenta como una alternativa a los métodos estadísticos clásicos, salvando las limitaciones de estos. En nuestro trabajo aplicamos la metodología rought sets a una base de datos, compuesta por 106 empresas, solicitantes de créditos, con el objeto de obtener aquellos ratios que mejor discriminan entre empresas sanas y fallidas, así como una serie de reglas de decisión que ayudarán a detectar las operaciones potencialmente fallidas, como primer paso en la modelización de la probabilidad de impago. Por último, enfrentamos los resultados obtenidos con los alcanzados con el análisis discriminante clásico, para concluir que la metodología de los rough sets presenta mejores resultados de clasificación, en nuestro caso.The new Capital Accord of June 2004 (Basel II) opens the way for and encourages credit entities to implement their own models for measuring financial risks. In the paper presented, we focus on the use of internal rating based (IRB) models for the assessment of credit risk and specifically on the approach to one of their components: probability of default (PD). In our study we apply the rough sets methodology to a database composed of 106 companies, applicants for credit, with the object of obtaining those ratios that discriminate best between healthy and bankrupt companies, together with a series of decision rules that will help to detect the operations potentially in default, as a first step in modelling the probability of default. Lastly, we compare the results obtained against those obtained using classic discriminant análisis. We conclude that the rough sets methodology presents better risk classification results.Junta de Andalucía P06-SEJ-0153

Knowledge reduction of dynamic covering decision information systems with varying attribute values

Knowledge reduction of dynamic covering information systems involves with the time in practical situations. In this paper, we provide incremental approaches to computing the type-1 and type-2 characteristic matrices of dynamic coverings because of varying attribute values. Then we present incremental algorithms of constructing the second and sixth approximations of sets by using characteristic matrices. We employ experimental results to illustrate that the incremental approaches are effective to calculate approximations of sets in dynamic covering information systems. Finally, we perform knowledge reduction of dynamic covering information systems with the incremental approaches