本文選題：圖約束　切入點：預(yù)聚類　出處：《計算機應(yīng)用研究》2017年05期

【摘要】：針對現(xiàn)有的威脅感知算法對樣本標注代價較大,并且在訓練分類器時只使用已標注的威脅樣本,提出了一種基于圖約束和預(yù)聚類的主動學習算法。該算法旨在通過降低標注威脅樣本的代價,并且充分利用未標注的威脅樣本對訓練分類器的輔助作用,訓練出更好的分類器以有效地感知威脅情景。該算法用已標注的威脅樣本集合訓練分類器,從未標注的威脅樣本集中挑選出最有價值的威脅樣本,并對其進行標注,再將標注后的威脅樣本加入已標注的樣本集中,同時刪去原來未標注樣本集中的此樣本,最后用新的已標注的威脅樣本集重新訓練分類器,直到滿足循環(huán)條件終止。仿真實驗表明,基于圖約束與預(yù)聚類的主動學習算法在達到目標準確率的同時降低了標注代價且誤報率較低,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】： 南京南瑞集團公司/國網(wǎng)電力科學研究院;
【基金】：企業(yè)自選科技資助項目
【分類號】：TP181;TP309
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本文編號：1667387