本文選題：圖約束　切入點(diǎn)：預(yù)聚類　出處：《計(jì)算機(jī)應(yīng)用研究》2017年05期

【摘要】：針對(duì)現(xiàn)有的威脅感知算法對(duì)樣本標(biāo)注代價(jià)較大,并且在訓(xùn)練分類器時(shí)只使用已標(biāo)注的威脅樣本,提出了一種基于圖約束和預(yù)聚類的主動(dòng)學(xué)習(xí)算法。該算法旨在通過降低標(biāo)注威脅樣本的代價(jià),并且充分利用未標(biāo)注的威脅樣本對(duì)訓(xùn)練分類器的輔助作用,訓(xùn)練出更好的分類器以有效地感知威脅情景。該算法用已標(biāo)注的威脅樣本集合訓(xùn)練分類器,從未標(biāo)注的威脅樣本集中挑選出最有價(jià)值的威脅樣本,并對(duì)其進(jìn)行標(biāo)注,再將標(biāo)注后的威脅樣本加入已標(biāo)注的樣本集中,同時(shí)刪去原來未標(biāo)注樣本集中的此樣本,最后用新的已標(biāo)注的威脅樣本集重新訓(xùn)練分類器,直到滿足循環(huán)條件終止。仿真實(shí)驗(yàn)表明,基于圖約束與預(yù)聚類的主動(dòng)學(xué)習(xí)算法在達(dá)到目標(biāo)準(zhǔn)確率的同時(shí)降低了標(biāo)注代價(jià)且誤報(bào)率較低,能夠有效地感知威脅情景,具有一定的研究意義。
[Abstract]:For the existing threat awareness algorithms, the cost of sample tagging is high, and only tagged threat samples are used in training classifier. This paper proposes an active learning algorithm based on graph constraint and preclustering, which aims to reduce the cost of tagging threat samples and make full use of unlabeled threat samples to assist the training classifier. A better classifier is trained to perceive the threat situation effectively. The algorithm uses the labeled threat sample set to train the classifier, and selects the most valuable threat sample from the untagged threat sample set and annotates it. Then the labeled threat sample is added to the labeled sample set, and the original unlabeled sample set is deleted. Finally, the new tagged threat sample set is used to retrain the classifier. The simulation results show that the active learning algorithm based on graph constraint and preclustering not only achieves the accuracy of target but also reduces the tagging cost and the false alarm rate is low. Has certain research significance.
【作者單位】： 南京南瑞集團(tuán)公司/國(guó)網(wǎng)電力科學(xué)研究院;
【基金】：企業(yè)自選科技資助項(xiàng)目
【分類號(hào)】：TP181;TP309
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本文編號(hào)：1667387