计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
17期
82-84,163
,共4页
流量分类%支持向量机%C4.5决策树%贝叶斯网%集成学习
流量分類%支持嚮量機%C4.5決策樹%貝葉斯網%集成學習
류량분류%지지향량궤%C4.5결책수%패협사망%집성학습
traffic classification%Support Vector Machine(SVM)%C4.5 decision tree%Bayesian Net(BN)%ensemble learning
针对流量分类问题中,传统单一的机器学习分类算法存在分类准确率难以提升和对网络环境变化适应能力不足的缺点,提出一种多分类器集成流量分类方法。该方法结合不同算法分类器的特点,使用多数投票和实例选择集成方法实现流量分类。对比实验表明,该方法在分类准确率和算法泛化性能上的表现均有所提升,对环境变化适应能力增强。但值得注意的是,该算法比独立分类法从实现复杂度和实际运行的时间复杂度均有所增加。
針對流量分類問題中,傳統單一的機器學習分類算法存在分類準確率難以提升和對網絡環境變化適應能力不足的缺點,提齣一種多分類器集成流量分類方法。該方法結閤不同算法分類器的特點,使用多數投票和實例選擇集成方法實現流量分類。對比實驗錶明,該方法在分類準確率和算法汎化性能上的錶現均有所提升,對環境變化適應能力增彊。但值得註意的是,該算法比獨立分類法從實現複雜度和實際運行的時間複雜度均有所增加。
침대류량분류문제중,전통단일적궤기학습분류산법존재분류준학솔난이제승화대망락배경변화괄응능력불족적결점,제출일충다분류기집성류량분류방법。해방법결합불동산법분류기적특점,사용다수투표화실례선택집성방법실현류량분류。대비실험표명,해방법재분류준학솔화산법범화성능상적표현균유소제승,대배경변화괄응능력증강。단치득주의적시,해산법비독립분류법종실현복잡도화실제운행적시간복잡도균유소증가。
Traditionally, in the area of the network traffic classification, there exists a problem that single learning algorithm lacks classification accuracy and is incapable of adapting to the dynamic network environment. Accordingly, it proposes a novel classification approach which is a combination of multi-classifier. This method combines the features of a range of classifiers and then achieves traffic classification by means of majority voting and instance selection. Moreover, comparative experiments show that this method improves the classification accuracy, the generalization performance and the ability to adapt to the dynamic network environment. However, it is worth noting that the method has a larger implement complexity and time complexity than these of single algorithm.