论文标题
电元树一种机器学习方法来推断electre tri b参数
Electre Tree A Machine Learning Approach to Infer Electre Tri B Parameters
论文作者
论文摘要
目的：本文提出了一种算法，可以引起（推断）所有或任何组合的eleptre tri-b参数。例如，决策者可以维持无冷漠，偏好和否决阈值的值，我们的算法可以找到标准权重，参考轮廓和lambda切割水平。我们的方法是受到机器学习合奏技术，随机森林的启发，为此，我们将我们的方法命名为Electre Tree算法。方法论：首先，我们生成一组Electre Tri-B模型，每个模型都求解了标准和替代方案的随机样本。每个样品都是用更换制成的，至少具有两个标准，含有替代方案的10％至25％。每个模型的参数都通过遗传算法优化，该算法可以使用有序群集或分配示例作为对优化的引用。最后，在优化阶段可以执行两个过程之后，第一个过程将合并所有模型，以这种方式查找引起的参数，在第二个过程中，每个替代方案被每个分离的模型分类（投票），大多数投票决定最终类。调查结果：我们注意到，关于投票程序，生成了非线性决策界限，它们可以适合分析具有相同性质的问题。相反，合并模型生成线性决策边界。原创性：启示eleptre tri-b参数是由一种集成技术制成的，该技术由一组参与生成健壮解决方案的多标准模型组成。
Purpose: This paper presents an algorithm that can elicitate (infer) all or any combination of ELECTRE Tri-B parameters. For example, a decision-maker can maintain the values for indifference, preference, and veto thresholds, and our algorithm can find the criteria weights, reference profiles, and the lambda cutting level. Our approach is inspired by a Machine Learning ensemble technique, the Random Forest, and for that, we named our approach as ELECTRE Tree algorithm. Methodology: First, we generate a set of ELECTRE Tri-B models, where each model solves a random sample of criteria and alternatives. Each sample is made with replacement, having at least two criteria and between 10% to 25% of alternatives. Each model has its parameters optimized by a genetic algorithm that can use an ordered cluster or an assignment example as a reference to the optimization. Finally, after the optimization phase, two procedures can be performed, the first one will merge all models, finding in this way the elicitated parameters, and in the second procedure each alternative is classified (voted) by each separated model, and the majority vote decides the final class. Findings: We have noted that concerning the voting procedure, non-linear decision boundaries are generated, and they can be suitable in analyzing problems with the same nature. In contrast, the merged model generates linear decision boundaries. Originality: The elicitation of ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a set of multicriteria models that are engaged in generating robust solutions.
