Conference Proceeding

Credit Rating Analysis with AFS Fuzzy Logic.

01/2005; DOI:10.1007/11539902_152 In proceeding of: Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part III
Source: DBLP

ABSTRACT In this paper, we propose a new machine learning approach based on AFS (Axiomatic Fuzzy Sets) fuzzy logic, in attempt to pro- vide a better model with interpretability. First, we will concisely present the AFS theory. Second, we will propose new membership functions for fuzzy sets and their logic operations. Third, we will design a new machine learning algorithm based on the new membership functions and their logic operations. This algorithm has two advantages. One is that it can mimic the human reasoning comprehensively and offers a far more flex- ible and effective means for the study of large-scale intelligent systems. Another is its simplicity in implementation and mathematical beauty in fuzzy theory. Finally, a credit data example is used to illustrate its effectiveness.

0 0
 · 
0 Bookmarks
 · 
75 Views
  • [show abstract] [hide abstract]
    ABSTRACT: Artificial intelligence is the study of how computer systems can simulate intelligent processes such as learning, reasoning, and understanding symbolic information in context. Axiomatic Fuzzy Set (AFS) theory, in which fuzzy sets (membership functions) and their logic operations are determined by a consistent algorithm according to the distributions of original data and the semantics of the fuzzy concepts, is applied to study some new techniques of feature selection, concept categorization and characteristic description; problems often encountered in artificial intelligence area such as machine learning and pattern recognition. These techniques developed under the framework of AFS theory in this paper are more simple and more interpretable than the conventional methods, since they imitate the human recognition process. In order to evaluate the effectiveness of the feature selection, the concept categorization and the characteristic description, these new techniques are applied to fuzzy clustering problems. Several benchmark data sets are used for this purpose. Clustering accuracies are comparable with or superior to the conventional algorithms such as FCM, k-means, and the new algorithm such as single point iterative weighted fuzzy C-means clustering algorithm.
    Appl. Soft Comput. 01/2010; 10:793-805.
  • [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: 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.

Full-text (2 Sources)

View
72 Downloads
Available from
Oct 17, 2013