基于支持向量機(jī)的多屬性決策方法
發(fā)布時(shí)間：2018-08-15 16:07
【摘要】：多屬性決策也稱有限多方案多目標(biāo)決策,即考慮多個(gè)方案,且每個(gè)方案有多個(gè)屬性進(jìn)行描述,這類問題在很多領(lǐng)域都很常見,因此該研究具有深刻的理論意義和廣泛的實(shí)際應(yīng)用背景。對于多屬性決策問題,往往希望能夠絕對客觀公正地給決策者最優(yōu)的備選方案,正是基于這種考慮,本文提出了利用支持向量機(jī)的原理,對決策問題進(jìn)行回歸分析擬合,從而得出決策模型的機(jī)制。支持向量機(jī)是一種基于統(tǒng)計(jì)學(xué)習(xí)理論的機(jī)器學(xué)習(xí)方法,它具有較為完備的理論基礎(chǔ)和較好的學(xué)習(xí)性能。目前統(tǒng)計(jì)學(xué)習(xí)理論正處于一個(gè)向?qū)嶋H應(yīng)用推廣的階段,支持向量機(jī)需要進(jìn)一步完善和改進(jìn),以滿足實(shí)際應(yīng)用的需求。支持向量機(jī)尤其對于小樣本、非線性問題展現(xiàn)了較為良好的性能。所以本文利用了支持向量機(jī)在多屬性決策問題上的適應(yīng)性,給出了基于支持向量機(jī)的多屬性決策方法。本文首先研究了支持向量回歸模型的參數(shù)問題,為了使得模型參數(shù)的選取更為迅速,引入了粒子群算法。將該模型應(yīng)用到一類多屬性決策問題中,對該問題進(jìn)行回歸擬合,并給出決策方案,通過對比實(shí)驗(yàn)看出優(yōu)化模型的性能。接著對多屬性決策中的期刊評價(jià)問題進(jìn)行實(shí)驗(yàn)分析。由于期刊評價(jià)問題的屬性間具有較強(qiáng)的相關(guān)性,這就使得一些回歸擬合方法具有限制性,而運(yùn)用支持向量回歸的方法可以不用考慮以上問題。最后,對屬性更多的更為復(fù)雜的多屬性決策問題進(jìn)行研究,引入主成分分析算法對這類問題進(jìn)行簡化,最大程度地去除冗余和不重要的屬性,再利用支持向量機(jī)模型擬合,大大提高了運(yùn)算效率,使得決策這一類問題變得簡便。
[Abstract]:Multi-attribute decision making is also called finite multi-scheme multi-objective decision making, that is, considering multiple schemes, and each scheme is described by multiple attributes. This kind of problem is very common in many fields. Therefore, this research has profound theoretical significance and extensive practical application background. For the multi-attribute decision making problem, we often hope to give the decision maker the best alternative scheme absolutely objectively and fairly. Based on this consideration, this paper puts forward the principle of using support vector machine to fit the decision problem by regression analysis. The mechanism of decision-making model is obtained. Support vector machine (SVM) is a machine learning method based on statistical learning theory. At present, the statistical learning theory is in a stage of popularization to practical application. Support vector machines need to be further improved to meet the needs of practical applications. Support vector machines (SVM), especially for small samples, show good performance for nonlinear problems. In this paper, the support vector machine (SVM) is applied to the multi-attribute decision making problem, and a multi-attribute decision making method based on the support vector machine (SVM) is presented. In this paper, the parameter problem of support vector regression model is studied. In order to select the model parameters more quickly, particle swarm optimization (PSO) algorithm is introduced. The model is applied to a class of multi-attribute decision making problems. The regression fitting of the problem is carried out, and the decision scheme is given. The performance of the optimized model is shown by comparison experiments. Then, the evaluation of journals in multi-attribute decision-making is analyzed experimentally. Because of the strong correlation between the attributes of periodical evaluation problems, some regression fitting methods are restricted, but the support vector regression method can be used to avoid the above problems. Finally, the more complex multi-attribute decision making problem with more attributes is studied. Principal component Analysis (PCA) algorithm is introduced to simplify this kind of problem, and the redundant and unimportant attributes are removed to the maximum extent, and then the support vector machine model is used to fit the problem. The operation efficiency is greatly improved, and the decision making problem becomes simple.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18
本文編號：2184715
[Abstract]:Multi-attribute decision making is also called finite multi-scheme multi-objective decision making, that is, considering multiple schemes, and each scheme is described by multiple attributes. This kind of problem is very common in many fields. Therefore, this research has profound theoretical significance and extensive practical application background. For the multi-attribute decision making problem, we often hope to give the decision maker the best alternative scheme absolutely objectively and fairly. Based on this consideration, this paper puts forward the principle of using support vector machine to fit the decision problem by regression analysis. The mechanism of decision-making model is obtained. Support vector machine (SVM) is a machine learning method based on statistical learning theory. At present, the statistical learning theory is in a stage of popularization to practical application. Support vector machines need to be further improved to meet the needs of practical applications. Support vector machines (SVM), especially for small samples, show good performance for nonlinear problems. In this paper, the support vector machine (SVM) is applied to the multi-attribute decision making problem, and a multi-attribute decision making method based on the support vector machine (SVM) is presented. In this paper, the parameter problem of support vector regression model is studied. In order to select the model parameters more quickly, particle swarm optimization (PSO) algorithm is introduced. The model is applied to a class of multi-attribute decision making problems. The regression fitting of the problem is carried out, and the decision scheme is given. The performance of the optimized model is shown by comparison experiments. Then, the evaluation of journals in multi-attribute decision-making is analyzed experimentally. Because of the strong correlation between the attributes of periodical evaluation problems, some regression fitting methods are restricted, but the support vector regression method can be used to avoid the above problems. Finally, the more complex multi-attribute decision making problem with more attributes is studied. Principal component Analysis (PCA) algorithm is introduced to simplify this kind of problem, and the redundant and unimportant attributes are removed to the maximum extent, and then the support vector machine model is used to fit the problem. The operation efficiency is greatly improved, and the decision making problem becomes simple.
【學(xué)位授予單位】：沈陽工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18
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