本文關鍵詞：基于社交網絡的信用評估模型的研究與實現(xiàn)　出處：《中北大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 微博信用 特征選擇 選擇性集成算法 K-means聚類 螢火蟲群優(yōu)化算法

【摘要】：互聯(lián)網科技的蓬勃發(fā)展使得社交網絡已成為人們之間相互溝通、社交等活動的重要手段。虛擬世界與現(xiàn)實世界已在不知不覺中相互交融并相互影響,過往人和人之間面對面的信任關系已延伸至網絡。社交網絡在給人們的生活帶來便利和趣味的同時,也引發(fā)了很多的困擾和威脅。社交失信事件時有發(fā)生,社交用戶的信用情況愈來愈引起人們的關注。如何監(jiān)管網絡環(huán)境,規(guī)范、約束網民的行為成為目前需要面對的重大問題,這個問題的解決對我國網絡誠信監(jiān)管建設具有重要的意義。論文通過對現(xiàn)有信貸個人信用評估模型研究發(fā)現(xiàn),單一的信用評估模型已趨向成熟,很難再有突破和擴展;而且很多的研究證明單一的信用評估模型弊端凸顯,通過集成學習,可以使單一模型進行互補,極大的改善單一模型的預測精度和穩(wěn)定性等性能,但是當單一模型數(shù)量過大時,會出現(xiàn)樣本學習時間過長,導致信用評估效率下降,也加大了機器存儲空間的要求。針對這些問題,“選擇性集成”的思想又被提出。選擇性集成方法目前已經被應用到很多的領域,并且均取得了較好的研究成果,但是在個人信用評估領域,研究成果相對來說還是較少,因此將選擇性集成方法應用于個人信用評估領域有很大的研究空間。針對社交網絡存在的網絡誠信問題,論文以新浪微博為研究對象,把“選擇性集成”的思想引入到微博信用評估領域,論文的主要工作內容如下:(1)結合微博平臺特點及現(xiàn)有評估指標體系存在的問題,重建了信用評估指標體系,通過對比實驗驗證了該指標體系的有效性;(2)將選擇性集成學習的思想引入到微博用戶信用評估領域,提出一種基于K-means聚類和螢火蟲群優(yōu)化選擇的KGSO選擇性集成算法;(3)將KGSO選擇性集成算法應用到微博用戶信用評估中,并通過對比實驗驗證了KGSO算法的有效性和優(yōu)越性;
[Abstract]:Rapid development of Internet technology makes the social network has become an important means of communication between people, social and other activities. The virtual world and the real world has imperceptibly interaction and mutual influence between the past, people face-to-face trust has been extended to the network. The social network bring convenience and fun at the same time to the people life, also caused a lot of problems and threats. Social credit events have occurred, social networking users credit has attracted more and more attention. How to regulate the network environment, norms, constraints the behavior of Internet users become the major problem facing the solution of this problem has important meaning to the network supervision of construction in China. Based on the existing credit evaluation model of credit evaluation model, the single is mature, very difficult to have a breakthrough and expansion; But many studies prove the obvious shortcomings of credit evaluation model through a single, integrated learning, can make a single model are complementary, improve the performance of the single model prediction accuracy and stability greatly, but when the single model number is too large, there will be a sample learning time is too long, resulting in a decline in credit evaluation efficiency, but also increased the machine the storage space requirements. To solve these problems, "selective integration" ideas have been proposed. Selective ensemble method has been applied to many fields, and have achieved good results, but in the field of personal credit evaluation, research is still relatively small, so the application of selective integration method in personal credit evaluation the field has a lot of research space. Aiming at the problem of network integrity of social networks exist, the Sina micro-blog as the research object, the "selective set "The idea is introduced to micro-blog credit evaluation, the main contents of this paper are as follows: (1) according to the existing micro-blog platform features and the existing evaluation index system, the reconstruction of the credit evaluation index system, through the experimental results verify the validity of the index system; (2) selective ensemble learning is introduced into micro-blog user credit evaluation, this paper puts forward a K-means clustering and glowworm swarm optimization algorithm based on selective ensemble selection of KGSO; (3) KGSO selective ensemble algorithm is applied to the micro-blog user credit evaluation, and through the experimental results verify the validity and superiority of KGSO algorithm;

【學位授予單位】：中北大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.09

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本文編號：1415563