Learning Conceptual Design Rules: A Rough Sets Approach
The paper presents the results of a feasibility study conducted in the area of learning conceptual design rules governing the selection of wind bracing components in steel skeleton structures of tall buildings. The study’s objectives were to compare decision rules produced by different learning systems using the same body of examples, and to formally verify these rules using the overall empirical error rate. The study was conducted using two learning systems, both based on the theory of rough sets: 1) System ROUGH which usually produces a large number of complex deterministic rules, 2) System DataLogic which can generate probabilistic rules, relatively simple and much fewer in number than in the case of ROUGH. All experiments were conducted using a collection of 374 examples of minimum weight (optimal) design of wind bracings in steel skeleton structures of tall buildings. The examples were prepared under identical design assumptions for a three bay skeleton structure of a tall building. They were produced using SODA, a computer software package for the analysis, design and optimization of steel structures. The paper gives a description of the learning experiments performed. It also provides a comparison of decision rules produced by DataLogic and Rough, and an analysis of empirical error rates obtained for the various collection of examples for ROUGH.
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