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Fuzzification of discrete attributes from financial data in fuzzy classification trees | IEEE Conference Publication | IEEE Xplore

Fuzzification of discrete attributes from financial data in fuzzy classification trees


Abstract:

Fuzzy decision trees have been successfully applied to both classification and regression problems by allowing gradual transitions to exist between attribute values. Meth...Show More

Abstract:

Fuzzy decision trees have been successfully applied to both classification and regression problems by allowing gradual transitions to exist between attribute values. Methodologies for fuzzification in fuzzy trees currently create such gradual transitions for continuous attributes. This is achieved by automatically creating fuzzy regions around tree nodes using an optimization algorithm or by using the knowledge of a human expert to create a series of fuzzy sets which are representative of the attributes domain. A problem occurs when trying to construct a fuzzy tree from real world data which comprises of only discrete or a mixture of discrete and continuous attributes. Discrete attribute values have no proximity to other values in the decision space, as there is no continuum between values. Consequently, within a fuzzy tree they are interpreted as crisp sets and contribute little towards the final outcome. This paper proposes a new approach for the fuzzification of discrete attributes in fuzzy decision trees. The approach ranks discrete values on the basis of their effect on the outcome rate and assigns a possibility of being a specific outcome. Experiments carried out on two real world financial datasets which contain a significant proportion of discrete attributes show improved classification accuracy compared with a crisp interpretation of such attributes within fuzzy trees.
Date of Conference: 20-24 August 2009
Date Added to IEEE Xplore: 02 October 2009
ISBN Information:
Print ISSN: 1098-7584
Conference Location: Jeju, Korea (South)

References

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