fuzzy decision-tree 中文意思是什麼
fuzzy decision-tree
解釋
模糊決策樹-
Network forensics is an important extension to present security infrastructure, and is becoming the research focus of forensic investigators and network security researchers. however many challenges still exist in conducting network forensics : the sheer amount of data generated by the network ; the comprehensibility of evidences extracted from collected data ; the efficiency of evidence analysis methods, etc. against above challenges, by taking the advantage of both the great learning capability and the comprehensibility of the analyzed results of decision tree technology and fuzzy logic, the researcher develops a fuzzy decision tree based network forensics system to aid an investigator in analyzing computer crime in network environments and automatically extract digital evidence. at the end of the paper, the experimental comparison results between our proposed method and other popular methods are presented. experimental results show that the system can classify most kinds of events ( 91. 16 ? correct classification rate on average ), provide analyzed and comprehensible information for a forensic expert and automate or semi - automate the process of forensic analysis
網路取證是對現有網路安全體系的必要擴展，已日益成為研究的重點.但目前在進行網路取證時仍存在很多挑戰:如網路產生的海量數據;從已收集數據中提取的證據的可理解性;證據分析方法的有效性等.針對上述問題，利用模糊決策樹技術強大的學習能力及其分析結果的易理解性，開發了一種基於模糊決策樹的網路取證分析系統，以協助網路取證人員在網路環境下對計算機犯罪事件進行取證分析.給出了該方法的實驗結果以及與現有方法的對照分析結果.實驗結果表明，該系統可以對大多數網路事件進行識別（平均正確分類率為91 . 16 ? ） ，能為網路取證人員提供可理解的信息，協助取證人員進行快速高效的證據分析 -
After analyzed the defaults of the fault dictionary method, several techniques to enhance the capability of the d. c. fault dictionary are presented. these include 1 ) using monte carlo analysis to get the node voltage tolerance, 2 ) using bayesian decision theory to direct the fuzzy set dividing, 3 ) selecting nodes by the fuzzy sets, 4 ) using the fault tree to diagnose the circuit ' s fault with varied sum of nodes
文中分析了字典法存在的問題，提出了改進方法，其中包括：用蒙特卡羅法求各節點電壓的容差域；用貝葉斯決策理論來指導模糊集劃分；以模糊集為特徵進行節點優選；依故障診斷樹進行變節點診斷。 -
( 2 ) in bidding stage qualitative and quantitative analysis shall be applied for risk analysis, including methods of expert scoring, decision tree, expected loss, hierarchy analysis, and fuzzy evaluation. which are applicable for analysis and evaluation of various risks during bidding period of the project
（ 2 ）投標階段中風險分析採用定性與定量相結合的思路方法，運用了專家打分法、決策樹方法、期望損失法、層次分析法、模糊評價法。對項目的投標過程各風險因素的分析和評價都具有一定的應用價值。 -
( 2 ) the reasonable describing about dynamic fuzzy decision tree from attributes treatment to building the tree and then pruning tree, and it provides a certain extent stated theory foundation for ulteriorly researching dynamic fuzzy decision tree and establishes a main concept frame of dynamic fuzzy decision tree
（ 2 ）對動態模糊決策樹從屬性處理到構建以及剪枝給出了合理的描述，形成了動態模糊決策樹的基本概念框架。 -
Thirdly, by introducing fuzzy theory into system evaluation, evaluating student, teaching, course resource, and function of whole system. fourthly, making use of learning from examples based on information theory, machine learning algorithm is improved and machine learning decision tree is realized. finally, on reasoning mechanism, combining means of two classes reasoning is taken
第三，在系統評價中引入了模糊理論，對學生、教學、課程資源以及系統的整體功能進行了評價；第四，採用基於信息論的示例學習，改進了決策樹學習演算法，並建立了機器學習決策樹；第五，在推理機制上，採取兩級推理相結合的方法進行推理，即用基於語義網路的模糊推理確定教學序列，用基於產生式規則的推理確定教學方法，並給出了詳細的推理演算法。 -
On the basis of the concepts of divide - and - conquer and rough - fine, dia build a set of algorithm models including two rough classifiers ( i. e. bdt and cmac ) and three fine classifiers ( i. e. clchfpp, cpn - wed, nne - ifm ) which integrate some techniques such as clustering, decision tree, neural networks, fuzzy logic and information fusion etc. and take advantage of them, so dia has a high accuracy and speed theoretically
Dia系統中演算法模型的設計基於「分而治之」和「由粗到精」的模式識別思想，具體構建了兩種粗分器（ bdt和cmac ）及三種精分器（ clc - fpp 、 cpn - wed和nne - ifm ） ，這些分類器的設計綜合了聚類法、決策樹、神經網路、模糊邏輯以及信息融合等技術的應用，並使其優勢互補，因此在理論上具有識別精度高及速度快的優點。 -
Because building optimal fuzzy decision tree is np - hard, it is necessary to study the heuristics
由於構建最優的模糊決策樹是np - hard ，因此，針對啟發式演算法的研究是非常必要的。 -
By analyzing expression between a and fuzzy entropy from the view of analytics, this paper analyses the relationship of between a and fuzzy entropy and the changing trend of fuzzy entropy function with the increase of a, then discusses the sensitivity of the parameter a to classification result such as total nodes, rule number, classification accuracy of fuzzy decision tree, proposes an experimental method of obtaining optimal a, it is proved by experiment that the optimal value a obtained by this method can make the classification result of fuzzy decision tree best, and therefore provides the academic evidence of selecting parameter a in order to gain the best classification result
本文在visualc + +軟體開發平臺及模糊id3演算法的基礎上，從解析的角度出發，通過分析參數與模糊熵之間的函數關系式，討論了隨著的增加，模糊熵函數的變化趨勢，進一步分析了參數對模糊決策樹的分類結果在訓練準確率、測試準確率、規則數等方面所表現出的敏感性，探討了得到最優參數的實驗方法。實驗證明，利用這一方法得到的最優參數的值，可以使模糊決策樹的分類結果達到最好的效果，從而為人們用模糊決策樹進行分類時選取參數以獲得最優的分類結果，提供了良好的理論依據。 -
At first, this thesis described the necessary of implementing dm in crm systems at on the basis of explaining the elementary concepts and principles of crm and dm, constructed a crm system framework with the center of dm. then, it ameliorated and expanded the models of traditional association rule and decision tree for classification, put forward association rule with time constraint and fuzzy decision tree for classification. the thesis amended traditional algorithms and showed the application methods of new models
論文首先從客戶關系管理和數據挖掘的基本概念和原理入手，闡明了在客戶關系管理中應用數據挖掘的必要性，構建了以數據挖掘為核心的crm系統框架；然後，論文改進和擴展了傳統的關聯規則和決策分類樹模型，提出了具有確定性時間約束的關聯規則和模糊決策分類樹，並修改了傳統的挖掘演算法，通過示例展示了新模型的應用方法。 -
With the studying of decision tree, the decision tree learning that only can describe the accurate characters is not adapted to the requirement describing the imprecise knowledge of a system. the world human being belong to and the domains issues within are dynamic. so in the real world, we hope the decision tree is capable of describing the dynamic fuzzy issues
隨著決策樹歸納學習研究的深入，具有精確描述特徵的決策樹歸納學習已經不能適應一個系統中不精確的知識表達的要求，同時由於人們所處的世界和問題所在的域都是時刻運動變化的，所以在現實問題中人們更希望決策樹能夠描述具有動態模糊性的問題。 -
The often - used classification is classification by decision tree induction, bayesian classification and bayesian belief networks, k - nearest neighbor classifiers, rough set theory and fuzzy set approaches
分類演算法常見的有判定樹歸納分類、貝葉斯分類和貝葉斯網路、 k -最臨近分類、粗糙集方法以及模糊集方法。 -
In building fuzzy decision tree, each expanded attribute ca n ' t classify the class label clearly like decision tree, but the cases covered with the attribute - values have some overlap. so the entire process of building trees is based on a significant level a, the import of a can reduce such overlap in some degree, decrease the uncertainty of classification and improve classification result
在模糊決策樹的產生過程中，用模糊熵選擇的擴展屬性不能像經典決策樹那樣將類清晰的分開，而是屬性術語所覆蓋的例子之間有一定的重疊，因此樹的整個產生過程在給定的顯著性水平的基礎上進行，參數的引入能在一定程度上減少這種重疊，從而減少分類的不確定性，提高模糊決策樹的分類結果。
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