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

Infinitary Combinatory Reduction Systems: Confluence

We study confluence in the setting of higher-order infinitary rewriting, in particular for infinitary Combinatory Reduction Systems (iCRSs). We prove that fully-extended, orthogonal iCRSs are confluent modulo identification of hypercollapsing subterms. As a corollary, we obtain that fully-extended, orthogonal iCRSs have the normal form property and the unique normal form property (with respect to reduction). We also show that, unlike the case in first-order infinitary rewriting, almost non-collapsing iCRSs are not necessarily confluent

Modularity of Convergence and Strong Convergence in Infinitary Rewriting

Properties of Term Rewriting Systems are called modular iff they are preserved under (and reflected by) disjoint union, i.e. when combining two Term Rewriting Systems with disjoint signatures. Convergence is the property of Infinitary Term Rewriting Systems that all reduction sequences converge to a limit. Strong Convergence requires in addition that redex positions in a reduction sequence move arbitrarily deep. In this paper it is shown that both Convergence and Strong Convergence are modular properties of non-collapsing Infinitary Term Rewriting Systems, provided (for convergence) that the term metrics are granular. This generalises known modularity results beyond metric \infty

A Rough Set Approach to Dimensionality Reduction for Performance Enhancement in Machine Learning

Machine learning uses complex mathematical algorithms to turn data set into a model for a problem domain. Analysing high dimensional data in their raw form usually causes computational overhead because the higher the size of the data, the higher the time it takes to process it. Therefore, there is a need for a more robust dimensionality reduction approach, among other existing methods, for feature projection (extraction) and selection from data set, which can be passed to a machine learning algorithm for optimal performance. This paper presents a generic mathematical approach for transforming data from a high dimensional space to low dimensional space in such a manner that the intrinsic dimension of the original data is preserved using the concept of indiscernibility, reducts, and the core of the rough set theory. The flue detection dataset available on the Kaggle website was used in this research for demonstration purposes. The original and reduced datasets were tested using a logistic regression machine learning algorithm yielding the same accuracy of 97% with a training time of 25 min and 11 min respectively

Canonized Rewriting and Ground AC Completion Modulo Shostak Theories : Design and Implementation

AC-completion efficiently handles equality modulo associative and commutative function symbols. When the input is ground, the procedure terminates and provides a decision algorithm for the word problem. In this paper, we present a modular extension of ground AC-completion for deciding formulas in the combination of the theory of equality with user-defined AC symbols, uninterpreted symbols and an arbitrary signature disjoint Shostak theory X. Our algorithm, called AC(X), is obtained by augmenting in a modular way ground AC-completion with the canonizer and solver present for the theory X. This integration rests on canonized rewriting, a new relation reminiscent to normalized rewriting, which integrates canonizers in rewriting steps. AC(X) is proved sound, complete and terminating, and is implemented to extend the core of the Alt-Ergo theorem prover.Comment: 30 pages, full version of the paper TACAS'11 paper "Canonized Rewriting and Ground AC-Completion Modulo Shostak Theories" accepted for publication by LMCS (Logical Methods in Computer Science