本文選題：隨機(jī)森林 + 指標(biāo)�。� 參考：《安徽大學(xué)》2017年碩士論文

【摘要】：目前,傳統(tǒng)的市場經(jīng)濟(jì)環(huán)境正在發(fā)生著巨大的演變。企業(yè)現(xiàn)在所面臨的不僅僅是以往常見的企業(yè)間的競爭,而是轉(zhuǎn)變?yōu)槠髽I(yè)在供應(yīng)鏈上的資源爭奪。而隨機(jī)森林算法成為現(xiàn)在在科學(xué)操作方面嶄新的一種使用方法。它主要被用來發(fā)掘大量數(shù)據(jù)中隱藏的那些可以使用的,能夠在現(xiàn)實(shí)中操作的信息。而本文就選擇隨機(jī)森林這種算法作為實(shí)驗(yàn)的依據(jù)。首先,對論文的研究背景,意義和方法進(jìn)行了簡單的描述。而目前來說,大多數(shù)企業(yè)在做供應(yīng)商評估時(shí)都沒有一套科學(xué)的體系,都是評估人員憑借著自己的經(jīng)驗(yàn)來進(jìn)行。這種方法存在較強(qiáng)的主觀性,而建立合理的評估體系對企業(yè)降低成本,減少風(fēng)險(xiǎn)有著很大的好處。但是好的體系的建立需要選出那些具有代表性的指標(biāo)。通過對國內(nèi)外的文獻(xiàn)進(jìn)行研究,選取指標(biāo)時(shí)要能夠嚴(yán)格的契合研究的目的;指標(biāo)體系的構(gòu)建要滿足:完善性,合理性,易操作性。在第二章中對隨機(jī)森林算法模型進(jìn)行了詳細(xì)的闡述。隨機(jī)森林是樹型分類組合器中的一種,對樣本數(shù)據(jù)的處理采用Bagging和隨機(jī)選擇特征的方式進(jìn)行。而在使用Bagging的方法進(jìn)行抽樣時(shí),會有一部分?jǐn)?shù)據(jù)不會被抽中,這部分?jǐn)?shù)據(jù)就可以用來估計(jì)模型的泛化誤差。同時(shí),通過實(shí)驗(yàn)證明得出,隨機(jī)森林模型的泛化誤差在樹的數(shù)目達(dá)到一定值時(shí),其收斂于一個(gè)有限值,所以利用這個(gè)原理可以確定森林中樹的數(shù)目。而根據(jù)本文研究的目的,選擇了 22個(gè)指標(biāo),由于隨機(jī)森林模型可以計(jì)算指標(biāo)體系的重要性,借助于R軟件,通過實(shí)驗(yàn)可以得到最終的指標(biāo)體系。那么在最后一個(gè)章節(jié)就是根據(jù)得到的指標(biāo)體系來建立隨機(jī)森林模型,并且通過隨機(jī)森林驗(yàn)證了其對噪聲具有很好的免疫能力。本文這種對供應(yīng)商通過建立模型對其進(jìn)行評估的方法,是值得進(jìn)行深入推廣研究的。實(shí)踐和理論的相互結(jié)合,證明了隨機(jī)森林有著很好的性能。但是,本文在研究中也有一些問題值得進(jìn)一步的思考,如選擇的數(shù)據(jù)量不是很大,可能會存在偏差,而且對存在的離群點(diǎn)沒有做任何的處理,同時(shí)指標(biāo)體系在篩選時(shí)并沒有進(jìn)行詳細(xì)的解釋。
[Abstract]:At present, the traditional market economy environment is undergoing a tremendous evolution. What enterprises are facing now is not only the competition among enterprises, but also the competition for resources in supply chain. The stochastic forest algorithm has become a new method in scientific operation. It is mainly used to extract information hidden in large amounts of data that can be used and can be manipulated in reality. In this paper, random forest algorithm is chosen as the experimental basis. First of all, the research background, significance and methods of the paper are briefly described. At present, most enterprises do not have a scientific system in supplier evaluation. This method has strong subjectivity, and the establishment of a reasonable evaluation system has great benefits to reduce the cost and reduce the risk. But the establishment of a good system requires the selection of representative indicators. Through the study of the domestic and foreign literature, we should be able to strictly fit the purpose of the research when selecting the index; the construction of the index system should be satisfied: perfect, reasonable, easy to operate. In the second chapter, the stochastic forest algorithm model is described in detail. Random forest is a kind of tree type classifier. The processing of sample data is carried out by Bagging and random selection. When sampling with Bagging, some of the data will not be extracted, which can be used to estimate the generalization error of the model. At the same time, it is proved by experiments that the generalization error of the stochastic forest model converges to a limited value when the number of trees reaches a certain value, so the number of trees in the forest can be determined by using this principle. According to the purpose of this paper, 22 indexes are selected. Because the stochastic forest model can calculate the importance of the index system, by means of R software, the final index system can be obtained through experiments. In the last chapter, the stochastic forest model is established according to the obtained index system, and its immunity to noise is proved by random forest. In this paper, the method of evaluating suppliers by establishing models is worthy of further study. The combination of practice and theory proves that stochastic forest has good performance. However, there are some problems worth further thinking in this paper. For example, the amount of data selected is not very large, there may be deviation, and there is no treatment of the outliers. At the same time, the indicator system in the screening and did not carry out a detailed explanation.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F274;TP18

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