【摘要】：針對傳統(tǒng)網(wǎng)絡(luò)流量分類方法準(zhǔn)確率不高、開銷較大且應(yīng)用領(lǐng)域受限等諸多問題,文中提出一種基于主動學(xué)習(xí)支持向量機(jī)的網(wǎng)絡(luò)流量分類方法。該方法采用基于OVA方法的多類支持向量機(jī)來進(jìn)行分類,首先,針對支持向量機(jī)參數(shù)選擇,提出了一種改進(jìn)的網(wǎng)格搜索法來尋求最優(yōu)參數(shù);然后,為了降低需要標(biāo)注的樣本數(shù),提出一個(gè)改進(jìn)的啟發(fā)式主動學(xué)習(xí)樣本查詢準(zhǔn)則;最后,基于上述方法構(gòu)造基于主動學(xué)習(xí)的多類支持向量機(jī)分類器。結(jié)果表明,該方法可以在需要標(biāo)注的樣本數(shù)非常少的情況下明顯提高網(wǎng)絡(luò)流量分類的準(zhǔn)確率和效率,僅需傳統(tǒng)方法所需11%的樣本數(shù)即可達(dá)到98.7%的分類準(zhǔn)確率。
[Abstract]:Aiming at the problems of the traditional network traffic classification methods, such as low accuracy, high overhead and limited application fields, a network traffic classification method based on active learning support vector machine (ALSVM) is proposed in this paper. This method uses multi-class support vector machines based on OVA method to classify. Firstly, an improved mesh search method is proposed to find the optimal parameters for parameter selection of support vector machines, and then, in order to reduce the number of samples that need to be labeled, An improved heuristic active learning sample query criterion is proposed. Finally, a multi-class support vector machine classifier based on active learning is constructed. The results show that this method can obviously improve the accuracy and efficiency of network traffic classification under the condition that the number of samples needed to be labeled is very small, and the accuracy of classification can reach 98.7% only with 11% of the sample number required by the traditional method.
【作者單位】： 西安科技大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;西北農(nóng)林科技大學(xué)信息工程學(xué)院;
【基金】：陜西省教育廳自然基金(2013JK1187) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(2452015194)
【分類號】：TP393.07

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相關(guān)期刊論文 前2條

1 沈元懌;;基于主動學(xué)習(xí)的資源優(yōu)化分配方案研究[J];佛山科學(xué)技術(shù)學(xué)院學(xué)報(bào)(自然科學(xué)版);2006年01期

2 顧名宇;;基于主動學(xué)習(xí)方法的網(wǎng)絡(luò)流分類研究[J];微電子學(xué)與計(jì)算機(jī);2011年09期

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