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Formalising optimal feature weight setting in case based diagnosis as linear programming problems

机译:将基于案例的诊断中的最优特征权重设置形式化为线性规划问题

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摘要

Many approaches to case based reasoning (CBR) exploit feature weight setting algorithms to reduce the sensitivity to distance functions. In this paper, we demonstrate that optimal feature weight setting in a special kind of CBR problems can be formalised as linear programming problems. Therefore, the optimal weight settings can be calculated in polynomial time instead of searching in exponential weight space using heuristics to get sub-optimal settings. We also demonstrate that out approach can be used to solve classification problems.
机译:基于案例的推理(CBR)的许多方法都利用特征权重设置算法来降低对距离函数的敏感性。在本文中,我们证明了可以将特殊类型的CBR问题中的最佳特征权重设置形式化为线性规划问题。因此,可以在多项式时间内计算最佳权重设置,而不用使用启发式方法在指数权重空间中搜索以获得次优设置。我们还证明了out方法可用于解决分类问题。

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