 The generalized dependency degree between attributesHaixuan Yang, Irwin King and Michael R. Lyu Journal of the American Society for Information Science and Technology, 2007, vol. 58, issue 14, 2280-2294 Abstract: Inspired by the dependency degree γ, a traditional measure in Rough Set Theory, we propose a generalized dependency degree, Γ, between two given sets of attributes, which counts both deterministic and indeterministic rules while γ counts only deterministic rules. We first give its definition in terms of equivalence relations and then interpret it in terms of minimal rules, and further describe the algorithm for its computation. To understand Γ better, we investigate its various properties. We further extend Γ to incomplete information systems. To show its advantage, we make a comparative study with the conditional entropy and γ in a number of experiments. Experimental results show that the speed of the new C4.5 using Γ is greatly improved when compared with the original C4.5R8 using conditional entropy, while the prediction accuracy and tree size of the new C4.5 are comparable with the original one. Moreover, Γ achieves better results on attribute selection than γ. The study shows that the generalized dependency degree is an informative measure in decision trees and in attribute selection. Date: 2007 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:58:y:2007:i:14:p:2280-2294 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Journal of the American Society for Information Science and Technology from Association for Information Science & Technology |
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