本文選題：移動(dòng)互聯(lián)網(wǎng) + 特征數(shù)據(jù)��； 參考：《計(jì)算機(jī)仿真》2017年02期

【摘要】：對(duì)移動(dòng)互聯(lián)網(wǎng)中特征數(shù)據(jù)準(zhǔn)確提取,可減少移動(dòng)互聯(lián)網(wǎng)的運(yùn)行負(fù)荷。進(jìn)行特征數(shù)據(jù)提取時(shí),應(yīng)分析不同數(shù)據(jù)屬性的區(qū)分能力,對(duì)移動(dòng)互聯(lián)網(wǎng)數(shù)據(jù)進(jìn)行屬性約簡(jiǎn),減少特征數(shù)據(jù)提取的工作量,但是傳統(tǒng)方法是通過(guò)獲取移動(dòng)互聯(lián)網(wǎng)數(shù)據(jù)集合的模糊粗糙近似,構(gòu)造移動(dòng)互聯(lián)網(wǎng)特征數(shù)據(jù)屬性集提取的目標(biāo)函數(shù),但是不能有效對(duì)移動(dòng)互聯(lián)網(wǎng)數(shù)據(jù)進(jìn)行屬性約簡(jiǎn),導(dǎo)致特征數(shù)據(jù)提取耗時(shí)長(zhǎng),效率低下的問(wèn)題。提出一種基于粒計(jì)算與區(qū)分能力的移動(dòng)互聯(lián)網(wǎng)中特征數(shù)據(jù)準(zhǔn)確提取方法。首先利用統(tǒng)計(jì)學(xué)中的分層抽樣技術(shù)將移動(dòng)互聯(lián)網(wǎng)初始數(shù)據(jù)集拆分為多個(gè)樣本子集(粒),并計(jì)算出每個(gè)粒上數(shù)據(jù)屬性的區(qū)分能力,融合于小生境免疫優(yōu)化理論,引入屬性集合的分類(lèi)近似標(biāo)準(zhǔn)作為數(shù)據(jù)屬性約簡(jiǎn)免疫優(yōu)化的親和度,然后生成小生境免疫共享機(jī)制,對(duì)移動(dòng)互聯(lián)網(wǎng)數(shù)據(jù)屬性約簡(jiǎn),最終建立移動(dòng)互聯(lián)網(wǎng)中特征數(shù)據(jù)準(zhǔn)確提取模型。仿真結(jié)果表明,所提方法移動(dòng)互聯(lián)網(wǎng)中特征數(shù)據(jù)提取精確度高,為更好地提升移動(dòng)互聯(lián)網(wǎng)服務(wù)質(zhì)量奠定了堅(jiān)實(shí)的基礎(chǔ)。
[Abstract]:Accurate extraction of feature data in mobile Internet can reduce the running load of mobile Internet. In order to reduce the workload of feature data extraction, we should analyze the distinguishing ability of different data attributes and reduce the attribute reduction of mobile Internet data. But the traditional method is to obtain fuzzy rough approximation of mobile Internet data set, and construct objective function of mobile Internet feature data attribute set extraction, but it can not effectively reduce mobile Internet data attribute. It leads to the problem of long time consuming and low efficiency of feature data extraction. This paper presents an accurate feature extraction method for mobile Internet based on granular computing and distinguishing ability. Firstly, the initial data set of mobile Internet is divided into several sample subsets by using stratified sampling technique in statistics, and the ability of distinguishing the attributes of each data on the grain is calculated, which is fused to the niche immune optimization theory. The classification approximation standard of attribute set is introduced as the affinity of data attribute reduction and immune optimization, and then the niche immune sharing mechanism is generated to reduce the attributes of mobile Internet data. Finally, an accurate model of feature data extraction in mobile Internet is established. The simulation results show that the proposed method has high accuracy of feature data extraction in mobile Internet, which lays a solid foundation for improving the QoS of mobile Internet.
【作者單位】： 內(nèi)蒙古財(cái)經(jīng)大學(xué)計(jì)算機(jī)系;
【分類(lèi)號(hào)】：TP393.01;TN929.5

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