本文選題：證據鏈推理 + 實體異構性��； 參考：《天津大學》2015年博士論文

【摘要】：數據驅動決策廣泛存在于工程實踐與管理中,對于數據融合中的知識推理理論和方法提出了新的挑戰(zhàn)。這些決策數據源自多傳感器、關系數據庫、不同經驗水平專家知識等,致使傳統(tǒng)的決策方法難以有效處理。因此,本文基于證據融合的決策框架,利用多源數據感知、信息傳遞與共享、CBR/RBR分類及智能決策等理論,研究了基于證據鏈推理的魯棒性分類決策。主要工作及創(chuàng)新性如下:(1)研究了層次關聯(lián)證據鏈推理方法。綜合評述了群決策的證據推理相關研究,并定義了證據鏈的知識結構、可信度序和指數型相似度。從屬性量、特征量和標識類別三層次,分析了用于分類決策的證據鏈性質。(2)研究了多源證據鏈推理模型,揭示了異構實體下數據驅動決策的推理機制。首先在單數據表證據鏈關聯(lián)基礎上,推導出查詢案例推論的融合可信度,改進了相似度頻率加權近鄰算法sf-NN,分析了類別錯誤標識對決策結構的影響。之后研究了證據鏈融合的正交合成規(guī)則,拓展了多數據表證據鏈推理模型mr FUER,利用多源證據鏈關聯(lián)算法x D-NN,提供解釋能力強的魯棒性決策。(3)為揭示時態(tài)系統(tǒng)在不同時間尺度下決策機制,將證據鏈從單一尺度推理拓展到多尺度推理,提出了多尺度特征的證據鏈推理模型ms FUER。使用相似度矩陣和辨識準則,構建了二次優(yōu)化的分類辨識框架,獲取了特征量的魯棒性權值參數。之后利用多尺度互信息,提出了二級混合整數優(yōu)化的多尺度特征優(yōu)選策略,用以解決特征組合增長問題,使得推理信息價值最大化。提出時態(tài)相似度的最近鄰算法ts-NN,其推理機制優(yōu)于傳統(tǒng)的單一尺度決策的推理機制。(4)為揭示系統(tǒng)在過程感知下的動態(tài)決策機制,將證據鏈從全域一次性推理拓展到序貫推理,發(fā)掘了感知模糊性下決策狀態(tài)轉移及可信度更新規(guī)律。放松了之前查詢的感知數據擁有完全信息的假設,在部分信息下構建了過程感知證據鏈推理模型md FUER,以單個特征量的特異度和靈敏度估算似然概率,提出基于狄利克雷函數的可信度更新算法df-BU,有效地實現了過程感知的魯棒性決策。(5)仿真實驗研究了基于FUER模型集的醫(yī)療決策支持。使用弗雷明漢心臟研究中4240個病歷、源自三個醫(yī)療機構的920例異構性病例和重癥監(jiān)護室的多參數智能監(jiān)護(MIMICII)的時態(tài)數據,研發(fā)了臨床決策支持系統(tǒng)原型及其共享的知識庫。對于h類和l類高低水平專家,從均衡準確度和證據鏈長度上評價了決策質量和效率改善效果,結果表明增強了分類決策魯棒性。
[Abstract]:Data-driven decision making is widely used in engineering practice and management, which poses a new challenge to the theory and method of knowledge reasoning in data fusion. These decision data are derived from multi-sensor, relational database, expert knowledge of different levels of experience and so on, which makes the traditional decision making method difficult to deal with effectively. Therefore, based on the decision framework of evidence fusion, using the theories of multi-source data perception, CBR / RBR classification and intelligent decision, this paper studies the robust classification decision based on evidence-chain reasoning. The main work and innovation are as follows: 1) the hierarchical correlation evidence chain reasoning method is studied. This paper reviews the research on evidence reasoning in group decision making, and defines the knowledge structure, credibility order and exponential similarity of evidence chain. From the three levels of attribute quantity, feature quantity and identification category, this paper analyzes the nature of evidence chain used in classification decision. It studies the reasoning model of multi-source evidence-chain, and reveals the reasoning mechanism of data-driven decision under heterogeneous entity. Firstly, based on the evidence chain association of single data table, the fusion credibility of query case inference is derived, the similarity frequency weighted nearest neighbor algorithm sf-NN is improved, and the influence of category error identification on decision structure is analyzed. Then the orthogonal synthesis rules of evidence chain fusion are studied. In this paper, the multi-datasheet evidence-chain reasoning model, mr fer, is extended, and the multi-source evidence-chain association algorithm x D-NN is used to provide robust decision making with strong explanatory power. It reveals the decision mechanism of temporal systems at different time scales. The evidence chain is extended from single scale reasoning to multi scale reasoning, and a multi scale feature based evidence chain reasoning model Ms fer is proposed. By using similarity matrix and identification criterion, a quadratic optimization classification and identification framework is constructed, and the robust weight parameters of the feature quantity are obtained. Then, using multi-scale mutual information, a multi-scale feature optimization strategy for two-level mixed integer optimization is proposed to solve the problem of feature combination growth and maximize the value of reasoning information. This paper presents a temporal similarity nearest neighbor algorithm ts-NN, whose reasoning mechanism is superior to that of the traditional single-scale decision making mechanism. In order to reveal the dynamic decision-making mechanism of the system under process awareness, the evidence chain is extended from global one-off reasoning to sequential reasoning. The rules of decision state transition and credibility update under perceived fuzziness are explored. After relaxing the assumption that the perceptual data of the previous query has complete information, a process perception evidence chain reasoning model, md fer, is constructed under partial information. The likelihood probability is estimated by the specificity and sensitivity of a single feature quantity. A credibility update algorithm df-BUbased on Delikley function is proposed. The robust decision making of process awareness is realized effectively. The simulation experiment is carried out to study the medical decision support based on FUER model set. The prototype of clinical decision support system and its shared knowledge base were developed by using the temporal data of 920 heterogeneous cases in three medical institutions and MIMICII in intensive care unit using 4240 medical records in the Framingham heart study. For high and low level experts of class h and class l, the effect of improving decision quality and efficiency is evaluated in terms of equilibrium accuracy and length of evidence chain. The results show that the robustness of classification decision is enhanced.
【學位授予單位】：天津大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：R541
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本文編號：1947393