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- W61833846 abstract "A hierarchical approach, in which a high- dimensional model is decomposed into series of low-dimensional sub-models connected in cascade, has been shown to be an effec- tive way to overcome the 'curse of dimensionality' problem. In- formation propagation through a cascade hierarchy of Linguistic Decision Trees (LDTs) based on label semantics forms a process of cascade decision making. In order to examine how a cascade hierarchy of LDTs works compared with a single LDT for mul- tiple attribute decision making, we developed genetic algorithm with linguistic ID3 in wrapper to find optimal cascade hierar- chies. Experiments have been carried out on the two bench- mark databases, Pima Diabetes and Wisconsin Breast Cancer databases from the UCI Machine Learning Repository. It is shown that an optimal cascade hierarchy of LDTs has better per- formance than a single LDT. The use of attribute hierarchies also greatly reduces the number of rules when the relationship be- tween a goal variable and input attributes is highly uncertain and nonlinear. a general multiple attribute hierarchy embedded with Linguis- tic Decision Trees (LDTs) based on Label Semantics (7). In this paper, we propose a cascade hierarchy approach embed- ded with LDTs representing transparent rules, and describe the process of information propagation through a cascade hi- erarchy. We then develop a genetic algorithm with the Lin- guistic ID3 (LID3) (9) algorithm in wrapper to optimise cas- cade hierarchies. The experiments are performed on bench- mark databases from the UCI Machine Learning Repository." @default.
- W61833846 created "2016-06-24" @default.
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- W61833846 date "2009-01-01" @default.
- W61833846 modified "2023-09-26" @default.
- W61833846 title "Optimal Cascade Linguistic Attribute Hierarchies for Decision Making" @default.
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