Conference Paper

# Credit Rating Analysis with AFS Fuzzy Logic.

DOI: 10.1007/11539902_152 Conference: Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part III

Source: DBLP

- [Show abstract] [Hide abstract]

**ABSTRACT:**This article presents a comprehensive methodology for the selection of logistic center location. The proposed methodology consists of two parts: (i) AFS (Axiomatic Fuzzy Set) clustering method (Liu, Wang, & Chai, 2005) has been studied further to effectively evaluate logistics center location, and (ii) TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)-based final selection. The criteria, which are relevant in the selection of logistics center site, have been analyzed and identified, and the logistics center site evaluation system is built by using modern principles of town planning and logistics. A case fifteen regional logistics center cities and thirteen criteria are studied and the numerical results show that the proposed evaluation framework is reasonable to identify logistics center location, and it is effective to determine the optimal logistics center location even with the interactive and interdependent criteria/attributes.Expert Syst. Appl. 01/2011; 38:7901-7908. - [Show abstract] [Hide abstract]

**ABSTRACT:**Municipal creditworthiness represents a very important measure of financial stability. Banks, supervisors, and other institutions rely upon ratings of creditworthiness to produce representations of the risk, nevertheless rating agencies never disclose evaluated parameters and their weight. This implies effort of scientific community to find accurate models for prediction of municipal creditworthiness rating. In this paper we present a classification model for municipal rating based on algorithms from the branch of artificial immune systems; Immunos-1, Immunos-2, Immunos-99, CLONALG and CLONCLAS.01/2010; 10:3-11. - [Show abstract] [Hide abstract]

**ABSTRACT:**In this paper, a cluster validity index proposed by Kim et al. [15] is analyzed, and a problem is discussed that the validity index faces in situations when there are well-separated clusters that themselves include subclusters. Based on this analysis, a new validity index is proposed. The new validity index employs a compactness measure and a separation measure. The compactness measure combines the fuzziness in the membership matrix (U)(U) with the geometrical compactness of the representation of the data set (X)(X) via the prototypes (V)(V). The separation measure is defined as the average value of the degrees of overlap of all possible pairs of fuzzy clusters in the system. The proposed index is tested and validated using several data sets. The results of the comparison show the superior effectiveness and reliability of the proposed index in comparison to other indices.Fuzzy Sets and Systems. 01/2010; 161:3014-3025.

Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.