【摘要】：針對(duì)目前微博轉(zhuǎn)發(fā)行為預(yù)測(cè)具有的特征選擇任意性、準(zhǔn)確率不高的問題,提出了融合異常檢測(cè)與隨機(jī)森林的微博轉(zhuǎn)發(fā)行為預(yù)測(cè)方法。首先,提取用戶基本特征、博文基本特征、博文內(nèi)容主題特征,并基于相對(duì)熵計(jì)算用戶活躍度、博文影響力;其次,通過結(jié)合過濾式與封裝式特征選擇方法篩選出關(guān)鍵特征組;最后,融合異常檢測(cè)與隨機(jī)森林算法,依據(jù)篩選后的關(guān)鍵特征組進(jìn)行微博轉(zhuǎn)發(fā)行為預(yù)測(cè),并利用袋外數(shù)據(jù)誤差估計(jì)設(shè)置隨機(jī)森林中的決策樹和特征數(shù)。在真實(shí)新浪微博數(shù)據(jù)集上與基于邏輯回歸、決策樹、樸素貝葉斯、隨機(jī)森林等算法的微博轉(zhuǎn)發(fā)行為預(yù)測(cè)方法進(jìn)行實(shí)驗(yàn)對(duì)比,結(jié)果表明所提方法的預(yù)測(cè)準(zhǔn)確率(90.5%)高于基準(zhǔn)方法中最優(yōu)的隨機(jī)森林方法的預(yù)測(cè)準(zhǔn)確率,同時(shí)驗(yàn)證了特征篩選方法的有效性。
[Abstract]:In order to solve the problem of arbitrary feature selection and low accuracy in Weibo forwarding behavior prediction, a Weibo forwarding behavior prediction method combining anomaly detection and random forest is proposed. Firstly, the basic features of users and the subject features of blog content are extracted, and the user activity and blog influence are calculated based on relative entropy. Secondly, the key feature groups are selected by combining filtering and encapsulation feature selection methods. Finally, combining anomaly detection and random forest algorithm, Weibo forwarding behavior is predicted according to the selected key feature groups, and the decision tree and feature number in random forest are set up by using out-of-bag data error estimation. The Weibo forwarding behavior prediction method based on logical regression, decision tree, naive Bays, random forest and other algorithms is compared with the Weibo forwarding behavior prediction method on the real Sina Weibo dataset. The results show that the prediction accuracy of the proposed method (90.5%) is higher than that of the optimal stochastic forest method in the benchmark method, and the effectiveness of the feature screening method is verified.
【作者單位】： 桂林電子科技大學(xué)計(jì)算機(jī)與信息安全學(xué)院;桂林電子科技大學(xué)廣西可信軟件重點(diǎn)實(shí)驗(yàn)室;
【基金】：廣西科技攻關(guān)項(xiàng)目(桂科攻1598019-6)資助
【分類號(hào)】：TP391.1;TP393.092

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