【摘要】：針對基于固定階Markov鏈模型的方法不能充分利用不同階次子序列結(jié)構(gòu)特征的問題,提出一種基于多階Markov模型的符號序列貝葉斯分類新方法。首先,建立了基于多階次Markov模型的條件概率分布模型;其次,提出一種附后綴表的n-階子序列后綴樹結(jié)構(gòu)和高效的樹構(gòu)造算法,該算法能夠在掃描一遍序列集過程中建立多階條件概率模型;最后,提出符號序列的貝葉斯分類器,其訓(xùn)練算法基于最大似然法學(xué)習(xí)不同階次模型的權(quán)重,分類算法使用各階次的加權(quán)條件概率進(jìn)行貝葉斯分類預(yù)測。在三個應(yīng)用領(lǐng)域?qū)嶋H序列集上進(jìn)行了系列實(shí)驗(yàn),結(jié)果表明:新分類器對模型階數(shù)變化不敏感;與使用固定階模型的支持向量機(jī)等現(xiàn)有方法相比,所提方法在基因序列與語音序列上可以取得40%以上的分類精度提升,且可輸出符號序列Markov模型最優(yōu)階數(shù)參考值。
[Abstract]:Aiming at the problem that the method based on fixed order Markov chain model can not make full use of the structural characteristics of different order subsequences, a new method of symbol sequence Bayesian classification based on multi-order Markov model is proposed. Firstly, the conditional probability distribution model based on multi-order Markov model is established. Secondly, a nth-order sub-sequence suffix tree structure with suffix table and an efficient tree construction algorithm are proposed. The algorithm can establish a multi-order conditional probability model in the process of scanning a sequence set. Finally, a Bayesian classifier for symbol sequences is proposed. Its training algorithm is based on the maximum likelihood method to learn the weights of different order models. The classification algorithm uses the weighted conditional probability of each order to predict Bayesian classification. A series of experiments are carried out on the actual sequence sets of three application fields. The results show that the new classifier is not sensitive to the change of model order, and compared with the existing methods such as support vector machine with fixed order model. The proposed method can improve the classification accuracy by more than 40% in gene sequence and speech sequence, and can output the optimal order reference value of symbol sequence Markov model.
【作者單位】： 福建農(nóng)林大學(xué)金山學(xué)院;福建師范大學(xué)數(shù)學(xué)與計(jì)算機(jī)科學(xué)學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61672157)~~
【分類號】：O211.62

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本文編號：2201717