【摘要】：貝葉斯分析方法是研究不確定性的一種方法,并用概率的大小來表示其不確定性,基于此方法建立的分類模型具有可解釋性、準(zhǔn)確率高等優(yōu)點(diǎn),目前在許多領(lǐng)域得到了廣泛應(yīng)用.而隨著我國經(jīng)濟(jì)的快速發(fā)展,信用評(píng)估也逐漸成為當(dāng)前值得關(guān)注的話題之一.針對(duì)信用評(píng)估數(shù)據(jù)的特點(diǎn),本文提出了基于隨機(jī)森林特征選擇的貝葉斯分類模型,并選取UCI數(shù)據(jù)庫中的German數(shù)據(jù)集進(jìn)行實(shí)證分析,結(jié)果表明:基于隨機(jī)森林特征選擇的思想,不但使得貝葉斯分類模型的結(jié)構(gòu)更加簡單,而且其獲得的分類效果更優(yōu).本文主要的工作和創(chuàng)新如下:(1)隨機(jī)森林是一種能容忍噪聲且穩(wěn)定性較高的智能學(xué)習(xí)算法,基于此算法的特征選擇可以進(jìn)行特征變量篩選,刪除其冗余不相關(guān)的特征屬性,又考慮到具有良好分類效果的樸素貝葉斯模型,本文構(gòu)建了基于隨機(jī)森林特征選擇的樸素貝葉斯分類模型(RF-NB).(2)在實(shí)際應(yīng)用中,考慮到樸素貝葉斯的“獨(dú)立性假設(shè)”往往不成立,為使模型更符合實(shí)際,樹增強(qiáng)樸素貝葉斯模型可以更好的表示特征屬性間存在的依賴關(guān)系,因此本文又構(gòu)建了基于隨機(jī)森林特征選擇的樹增強(qiáng)樸素貝葉斯分類模型(RF-TAN).(3)將基于隨機(jī)森林特征選擇的貝葉斯分類模型應(yīng)用到German數(shù)據(jù)信用評(píng)估指導(dǎo)中去,用于驗(yàn)證所提出的RF-NB和RF-TAN分類模型的分類效果,并與未進(jìn)行特征選擇的NB模型和未進(jìn)行特征選擇的TAN模型進(jìn)行實(shí)驗(yàn)對(duì)比.實(shí)驗(yàn)結(jié)果表明:RF-NB和RF-TAN模型的分類效果顯然優(yōu)于NB、TAN模型.
[Abstract]:Bayesian analysis method is a method to study uncertainty, and its uncertainty is represented by the size of probability. The classification model based on this method has the advantages of interpretability, high accuracy and so on. At present, it has been widely used in many fields. With the rapid development of China's economy, credit evaluation has gradually become one of the topics worth paying attention to. According to the characteristics of credit evaluation data, a Bayesian classification model based on stochastic forest feature selection is proposed in this paper, and the German data set in UCI database is selected for empirical analysis. The results show that: based on the idea of stochastic forest feature selection, Not only the structure of Bayesian classification model is simpler, but also the classification effect is better. The main works and innovations of this paper are as follows: (1) Random forest is an intelligent learning algorithm which can tolerate noise and is more stable. The feature selection based on this algorithm can be used to filter feature variables and delete its redundant and irrelevant feature attributes. Considering the naive Bayesian model with good classification effect, this paper constructs a naive Bayesian classification model based on stochastic forest feature selection (RF-NB). (2) in practical application, considering that the "independence hypothesis" of naive Bayes is often not valid. In order to make the model more realistic, the tree enhanced naive Bayes model can better represent the dependency between the feature attributes. Therefore, a tree enhanced naive Bayesian classification model (RF-TAN). (3) based on stochastic forest feature selection is constructed. The Bayesian classification model based on stochastic forest feature selection is applied to the guidance of German data credit evaluation. It is used to verify the classification effect of the proposed RF-NB and RF-TAN classification models, and compared with the NB model without feature selection and the TAN model without feature selection. The experimental results show that the classification effect of RF-NB and RF-TAN model is obviously better than that of NB,TAN model.
【學(xué)位授予單位】：華北水利水電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 葉曉楓;魯亞會(huì);;基于隨機(jī)森林融合樸素貝葉斯的信用評(píng)估模型[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2017年02期

2 吳信東;何進(jìn);陸汝鈐;鄭南寧;;從大數(shù)據(jù)到大知識(shí):HACE+BigKE[J];自動(dòng)化學(xué)報(bào);2016年07期

3 周美琴;陳詩旭;袁鼎榮;朱新華;;一種單位代價(jià)收益決策樹剪枝算法[J];計(jì)算機(jī)工程與科學(xué);2016年05期

4 李進(jìn);;基于隨機(jī)森林算法的綠色信貸信用風(fēng)險(xiǎn)評(píng)估研究[J];金融理論與實(shí)踐;2015年11期

5 趙煜;邵必林;邊根慶;宋丹;;面向不平衡微博數(shù)據(jù)集的轉(zhuǎn)發(fā)行為預(yù)測方法[J];計(jì)算機(jī)應(yīng)用;2015年07期

6 肖進(jìn);劉敦虎;顧新;汪壽陽;;銀行客戶信用評(píng)估動(dòng)態(tài)分類器集成選擇模型[J];管理科學(xué)學(xué)報(bào);2015年03期

7 劉敏;郎榮玲;曹永斌;;隨機(jī)森林中樹的數(shù)量[J];計(jì)算機(jī)工程與應(yīng)用;2015年05期

8 苗紅星;余建坤;;基于決策樹的ID3算法和C4.5算法的比較[J];現(xiàn)代計(jì)算機(jī)(專業(yè)版);2014年15期

9 孟杰;;隨機(jī)森林模型在財(cái)務(wù)失敗預(yù)警中的應(yīng)用[J];統(tǒng)計(jì)與決策;2014年04期

10 姚明海;趙連朋;劉維學(xué);;基于特征選擇的Bagging分類算法研究[J];計(jì)算機(jī)技術(shù)與發(fā)展;2014年04期

相關(guān)碩士學(xué)位論文 前1條

1 高金玲;基于Logistic回歸的中小型企業(yè)信用評(píng)估模型研究[D];西北師范大學(xué);2014年

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