本文選題：IPTV　切入點(diǎn)：機(jī)器學(xué)習(xí)　出處：《南京郵電大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在中國,三網(wǎng)融合正大力推進(jìn),交互式網(wǎng)絡(luò)電視(Internet Protocol Television,IPTV)作為三網(wǎng)融合最合適的著力點(diǎn),有著十分巨大的潛力,因此對(duì)IPTV的研究也成為了當(dāng)下的熱點(diǎn)。然而,運(yùn)營商傳統(tǒng)的運(yùn)維方法主要是根據(jù)用戶的投訴來排除設(shè)備故障,這種方法時(shí)效性差,并且需要大量運(yùn)維人員,造成人員冗余,已經(jīng)跟不上時(shí)代的發(fā)展。為了保證用戶收看IPTV的體驗(yàn),IPTV業(yè)務(wù)迫切需要一種更合理,更有效的用戶報(bào)障預(yù)測(cè)算法作為代替。同時(shí),隨著各類計(jì)算機(jī)性能的迅速提高,機(jī)器學(xué)習(xí)與社會(huì)各個(gè)領(lǐng)域結(jié)合的也越發(fā)緊密。本論文從機(jī)器學(xué)習(xí)的角度出發(fā),主要研究了基于機(jī)器學(xué)習(xí)的IPTV故障預(yù)測(cè)中涉及的一些關(guān)鍵問題,主要的研究?jī)?nèi)容如下:(1)本論文提出了基于F-Score與互信息的Relief特征選擇算法。Relief特征選擇算法具有簡(jiǎn)單明了,運(yùn)算速度快等優(yōu)點(diǎn),并且選擇的特征子集具有相當(dāng)優(yōu)異的性能,然而它對(duì)冗余特征的選擇能力較弱。由于Fisher Score對(duì)特征的類別也具有很好的區(qū)分能力,本論文將Fisher Score加入Relief算法中,以此進(jìn)一步提高Relief算法的優(yōu)點(diǎn),同時(shí)為了減少冗余特征,本論文也將互信息與Relief相結(jié)合。在多個(gè)數(shù)據(jù)集上的實(shí)驗(yàn)表明基于F-Score與互信息的Relief特征選擇算法相比原算法的分類準(zhǔn)確率得到提高。(2)本論文提出了基于權(quán)重限制與F1值的AdaBoost算法。AdaBoost分類算法簡(jiǎn)單穩(wěn)定,而且不容易過擬合,針對(duì)AdaBoost算法在分類過程中容易對(duì)異常點(diǎn)賦予較大權(quán)重導(dǎo)致算法失衡和分類錯(cuò)誤率不適合用于非均衡數(shù)據(jù)集的缺陷,本論文對(duì)樣本的權(quán)值做出了限制,并且綜合考慮F1值和分類錯(cuò)誤率對(duì)樣本權(quán)值的影響,在AdaBoost算法的基礎(chǔ)上提出了基于權(quán)重限制與F1值的AdaBoost算法,實(shí)驗(yàn)表明該算法可以有效提高分類準(zhǔn)確率。(3)本論文將基于F-Score與互信息的Relief特征選擇算法與基于權(quán)重限制與F1值的Ada Boost算法應(yīng)用于IPTV用戶報(bào)障預(yù)測(cè)。本論文對(duì)IPTV的各種指標(biāo)數(shù)據(jù)進(jìn)行分析和預(yù)處理,然后使用基于F-Score和互信息的Relief算法和基于權(quán)重限制與F1值的AdaBoost算法對(duì)IPTV數(shù)據(jù)進(jìn)行用戶報(bào)障預(yù)測(cè),實(shí)驗(yàn)結(jié)果表明改進(jìn)后的算法與原算法相比的預(yù)測(cè)準(zhǔn)確率得到提高。
[Abstract]:In China, tri-network convergence is being vigorously promoted. As the most suitable point of three-network convergence, interactive network television (IPTV) has great potential. Therefore, the research on IPTV has become a hot spot. Operators' traditional operation and maintenance methods are mainly based on customer complaints to troubleshoot equipment, this method is inefficient, and requires a large number of operation and maintenance personnel, resulting in personnel redundancy, In order to ensure users watch the experience of IPTV service, we urgently need a more reasonable and effective algorithm to predict the obstacle of users as a substitute. At the same time, with the rapid improvement of the performance of all kinds of computers, The combination of machine learning and various fields of society is becoming more and more close. From the point of view of machine learning, this paper mainly studies some key problems involved in IPTV fault prediction based on machine learning. The main research contents are as follows: (1) in this paper, a feature selection algorithm for Relief based on F-Score and mutual information. Relief feature selection algorithm has the advantages of simplicity, fast operation and so on, and the selected feature subset has excellent performance. However, its ability to select redundant features is weak. Because Fisher Score also has a good ability to distinguish feature categories, this paper adds Fisher Score to Relief algorithm to further improve the advantages of Relief algorithm and reduce redundant features. This paper also combines mutual information with Relief. Experiments on multiple data sets show that the classification accuracy of Relief feature selection algorithm based on F-Score and mutual information is improved compared with the original algorithm. The AdaBoost algorithm of F1 value. AdaBoost classification algorithm is simple and stable. And it is not easy to fit. In order to solve the problem that AdaBoost algorithm is easy to give outliers a large weight in the process of classification, the algorithm is unbalanced and the classification error rate is not suitable for non-equilibrium data sets, so this paper limits the weight of samples. Considering the influence of F1 value and classification error rate on sample weight, a AdaBoost algorithm based on weight restriction and F1 value is proposed on the basis of AdaBoost algorithm. Experiments show that this algorithm can effectively improve the classification accuracy.) in this paper, the Relief feature selection algorithm based on F-Score and mutual information and the Ada Boost algorithm based on weight limit and F1 value are applied to IPTV user barrier prediction. Analysis and preprocessing of various indicator data, Then the Relief algorithm based on F-Score and mutual information and the AdaBoost algorithm based on weight limit and F1 value are used to predict the IPTV data. The experimental results show that the prediction accuracy of the improved algorithm is higher than that of the original algorithm.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN949.292;TP181

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相關(guān)期刊論文 前1條

1 李良;邱曉彤;趙強(qiáng);馬紹良;;基于數(shù)據(jù)挖掘的IPTV QoE評(píng)價(jià)方法[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2016年11期

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本文編號(hào)：1635249