本文選題：多標(biāo)簽分類 + 多標(biāo)簽類別不均衡�。� 參考：《南京大學(xué)》2016年碩士論文

【摘要】：帕金森病(Parkinson's Disease, PD)是一種在中老年人中常見的慢性中樞神經(jīng)系統(tǒng)變性疾病。中醫(yī)對帕金森病的研究源遠(yuǎn)流長,對帕金森的證型也是眾說風(fēng)云。結(jié)合多年的中醫(yī)診治經(jīng)驗,現(xiàn)代中醫(yī)確定了帕金森病的五種證型,并認(rèn)為帕金森患者最多同時伴有具有主次之分的兩個證型。為了規(guī)范化帕金森病的中醫(yī)診斷過程,現(xiàn)代中醫(yī)提出了涵蓋帕金森病相關(guān)臨床癥狀的帕金森中醫(yī)量表。對于如何從量表中的癥狀推斷出具體的證型,中醫(yī)界依然無法達(dá)成共識,診斷仍以經(jīng)驗為主。本文將多標(biāo)簽學(xué)習(xí)運用到中醫(yī)診斷帕金森過程中,對證型進(jìn)行主次分離,利用多標(biāo)簽算法發(fā)掘癥狀與證型中潛藏的相互關(guān)系,試圖為中醫(yī)診斷過程提供輔助決策。本文主要工作：1).針對將多標(biāo)簽應(yīng)用于中醫(yī)診斷帕金森領(lǐng)域,量表的癥狀作為特征屬性,主次分離后的證型作為標(biāo)簽。根據(jù)次證的稀疏性,介紹了帕金森數(shù)據(jù)集中存在的較為嚴(yán)重的多標(biāo)簽類別不均衡問題。2).針對多標(biāo)簽不均衡中小類樣本缺乏數(shù)據(jù)表示的問題,基于貢獻(xiàn)度樣本的區(qū)分以及異常數(shù)據(jù)樣本過濾的思想,提出了一種適應(yīng)型小類樣本合成算法。算法從數(shù)據(jù)層面上很好的解決了多標(biāo)簽類別不均衡問題,相比于已有的多標(biāo)簽重采樣算法獲得了更好的實驗結(jié)果。3).針對標(biāo)簽相關(guān)性對多標(biāo)簽不均衡的影響,基于標(biāo)簽子集構(gòu)建以及欠采樣集成的思想,提出了基于標(biāo)簽子集樣本欠采樣集成算法。實驗結(jié)果表明算法相比于已有的多標(biāo)簽算法,在帕金森數(shù)據(jù)集以及多個公共數(shù)據(jù)集上能夠更好的解決不均衡現(xiàn)象。
[Abstract]:Parkinson's disease (PD) is a common chronic central nervous system degeneration in the elderly. The research on Parkinson's disease in TCM has a long history, and the syndrome type of Parkinson's disease is also popular. Combined with many years of experience in the diagnosis and treatment of Chinese medicine, modern Chinese medicine has determined the five types of Parkinson's disease, and thinks that Parkinson's patients are accompanied by two syndromes with primary and secondary types at most. In order to standardize the diagnosis of Parkinson's disease (PD), a Chinese medicine scale (TCM), which covers the clinical symptoms of Parkinson's disease (PD), has been proposed by modern Chinese medicine (TCM). There is still no consensus on how to deduce the specific syndromes from the symptoms of the scale, and the diagnosis is still based on experience. In this paper, multi-label learning is applied to the diagnosis of Parkinson's disease in traditional Chinese medicine (TCM), the main and secondary syndromes are separated, and the interrelation between symptoms and syndromes is explored by using multi-label algorithm, in order to provide auxiliary decision for the diagnosis process of TCM. The main work of this paper is 1: 1. In view of the application of multiple labels in the field of diagnosis of Parkinson's disease in traditional Chinese medicine, the symptom of the scale is regarded as the characteristic attribute, and the syndrome type after primary and secondary separation is used as the label. According to the sparsity of secondary syndromes, this paper introduces the serious multi-label class imbalance problem in Parkinson's dataset. In order to solve the problem of lack of data representation for small class samples in multi-label disequilibrium, an adaptive small class sample synthesis algorithm is proposed based on the distinction of contribution samples and the idea of abnormal data sample filtering. The algorithm solves the problem of multi-label class imbalance from the data level, and gets better experimental results than the existing multi-label resampling algorithm. Based on the idea of label subset construction and under-sampling integration, a sample under-sampling ensemble algorithm based on label subset is proposed for the influence of label correlation on multi-label imbalance. The experimental results show that compared with the existing multi-label algorithm, the algorithm can solve the imbalance better in Parkinson's datasets and common datasets.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP301.6;R277.7

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本文編號：1989161