本文關(guān)鍵詞： 多分類 支持向量機 基金評價　出處：《北京交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：支持向量機是基于統(tǒng)計學(xué)習(xí)理論解決機器學(xué)習(xí)問題的一種新工具,它最初由Vapnik及其同事于20世紀(jì)90年代提出,隨著統(tǒng)計學(xué)習(xí)理論的完善,近年來其在算法研究及實際應(yīng)用方面都取得了突破性進展,其中多分類支持向量機的研究更是當(dāng)前的熱點。 本文所做的主要工作有： 一、對支持向量機多分類算法的理論基礎(chǔ)與算法思想進行了總體闡述,并對各種多分類算法的性能進行對比研究。針對現(xiàn)有多分類算法存在“拒分區(qū)域”、SVM“錯誤累積效應(yīng)”的不足,提出了改進后的基于二叉樹的多分類支持向量機。本文創(chuàng)新性的引入了類分散度的概念,構(gòu)造最佳分類順序二叉樹結(jié)構(gòu),改進現(xiàn)有算法在推廣能力方面的缺陷,經(jīng)過算法實現(xiàn)并帶入UCI數(shù)據(jù)集驗證,新算法在分類精度和時間上表現(xiàn)出優(yōu)于傳統(tǒng)的OAA-SVMs、OAO-SVMs、DT-SVMs算法。 二、詳細總結(jié)了國內(nèi)外現(xiàn)有基金評級方法,介紹比較經(jīng)典的基金評價方法體系,并對現(xiàn)有的評價體系的優(yōu)缺點進行總結(jié)評述。 三、建立基于多分類支持向量機的基金評價模型。以基金各項評價指標(biāo)作為輸入向量,使用主成分分析法進行特征提取,并選擇適當(dāng)?shù)暮撕瘮?shù)及通過交叉驗證獲得最佳模型參數(shù),對訓(xùn)練集進行訓(xùn)練得到分類函數(shù),再使用預(yù)測樣本集進行驗證分析得到預(yù)測樣本基金的評級。此外,本文使用兩種多分類支持向量機算法——OAA-SVMs與改進DT-SVMs進行基金評價實證分析,分類準(zhǔn)確度最高達到80%。結(jié)果表明支持向量機方法對我國基金業(yè)績評價有良好的可行性和有效性,并且改進DT-SVMs在分類精度和分類時間上都優(yōu)于OAA-SVMs。
[Abstract]:Support vector machine (SVM) is a new tool for solving machine learning problems based on statistical learning theory. It was first proposed by Vapnik and his colleagues in 1990s, with the improvement of statistical learning theory. In recent years, it has made a breakthrough in algorithm research and practical application, among which the research of multi-classification support vector machine is a hot spot. The main work of this paper is as follows:. First, the theory foundation and algorithm idea of support vector machine multi-classification algorithm are introduced. A comparative study of the performance of various multi-classification algorithms is carried out. In view of the shortcomings of the existing multi-classification algorithms, such as "rejection region" and "error accumulation effect" of SVM, This paper proposes an improved multi-classification support vector machine based on binary tree. This paper innovatively introduces the concept of class dispersion, constructs the structure of the best classification sequence binary tree, and improves the shortcomings of the existing algorithms in popularizing ability. The new algorithm is better than the traditional OAA-SVMsOO-SVMsN DT-SVMs algorithm in classification accuracy and time. Secondly, the paper summarizes the existing fund rating methods at home and abroad, introduces the classical fund evaluation method system, and summarizes the advantages and disadvantages of the existing evaluation system. Thirdly, a fund evaluation model based on multi-classification support vector machine (SVM) is established. The evaluation indexes of the fund are used as input vectors, and the principal component analysis (PCA) is used for feature extraction. The best model parameters are obtained by selecting appropriate kernel function and cross validation. The classification function is obtained by training the training set, and the rating of the forecast sample fund is obtained by using the forecast sample set to verify and analyze. In this paper, two kinds of multi-classification support vector machines (OAA-SVMs) and improved DT-SVMs are used to carry out the empirical analysis of fund evaluation. The results show that the classification accuracy is as high as 80%. The results show that the SVM method is feasible and effective for fund performance evaluation in China. And the improved DT-SVMs is superior to OAA-SVMsin classification accuracy and classification time.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP18;F832.51

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