本文關(guān)鍵詞：基于改進Adaboost的信用評價方法　出處：《運籌與管理》2017年02期 　論文類型：期刊論文

  更多相關(guān)文章： 信用評價方法 Adaboost 分歧度 誤分代價

【摘要】：網(wǎng)絡(luò)借貸環(huán)境下基于Adaboost的信用評價方法具有較高的基分類器分歧度和樣本誤分代價�，F(xiàn)有研究沒有考慮分歧度和誤分代價對基分類器樣本權(quán)重的影響,從而降低了網(wǎng)絡(luò)借貸信用評價結(jié)果的有效性。為此,提出一種基于改進Adaboost的信用評價方法。該方法根據(jù)基分類器的誤分率,樣本在不同基分類器上分類結(jié)果的分歧程度,以及樣本的誤分代價等因素,調(diào)整Adaboost模型的樣本賦權(quán)策略,使得改進后的Adaboost模型能夠?qū)Ψ诸惱щy樣本和誤分代價高的樣本實施有針對性的學(xué)習(xí),從而提高網(wǎng)絡(luò)借貸信用評價結(jié)果的有效性�；谂呐馁J平臺數(shù)據(jù)的實驗結(jié)果表明,提出的方法在分類精度和誤分代價等方面顯著優(yōu)于傳統(tǒng)的基于Adaboost的信用評價方法。
[Abstract]:Credit evaluation Adaboost method has higher base classifier divergence and sample misclassification cost based on network lending environment. The existing studies do not consider differences and misclassification cost of base classifier sample weight, thereby reducing the effectiveness of online lending credit evaluation results. Therefore, a method is proposed to improve the credit rating of Adaboost based on this method. According to the classification error rate of the base classifier, the degree of divergence of samples in different base classifiers on the classification results, and sample misclassification cost and other factors, adjust the Adaboost model of the sample weighting strategy, the improved Adaboost model can implement targeted learning difficulties on the classification and sample of misclassification cost the effectiveness of the network so as to improve the credit evaluation results. A pat on the loan platform based on the experimental results, the proposed method on classification accuracy and error It is significantly better than the traditional Adaboost based credit evaluation method.

【作者單位】： 合肥工業(yè)大學(xué)管理學(xué)院;
【基金】：國家自然科學(xué)基金項目(71571059,71331002) 教育部人文社會科學(xué)規(guī)劃基金項目(15YJA630010)
【分類號】：F724.6;F832.4
【正文快照】： 0引言信用評價能夠有效地緩解借貸雙方間的信息不對稱,降低違約風(fēng)險與交易成本。傳統(tǒng)的信用評價方法可以分為統(tǒng)計學(xué)方法、人工智能方法和以風(fēng)險價值為基礎(chǔ)的方法[1~4]。近年來,組合信用評價方法逐漸受到學(xué)者的關(guān)注[5]。常用的組合方式包括串行組合[6]、并行組合[7]以及基于Bag，

本文編號：1419972