本文選題：樸素貝葉斯　切入點：文本信息分類　出處：《東北農(nóng)業(yè)大學》2015年碩士論文

【摘要】：當今互聯(lián)網(wǎng)高速發(fā)展,人類社會邁入網(wǎng)絡信息爆炸時代,這帶來了網(wǎng)絡上農(nóng)業(yè)知識信息的激增,給農(nóng)業(yè)從業(yè)者帶來了農(nóng)業(yè)信息查找的便利。知識意味著財富,農(nóng)業(yè)從業(yè)者從這些農(nóng)業(yè)信息中擷取財富信息,然而,海量的農(nóng)業(yè)知識信息不意味著可以快速有效的查詢出所需信息,農(nóng)業(yè)領域細化信息的快速定位與分類查找是必要與必須的。本文以農(nóng)業(yè)信息搜索引擎分類器為研究對象,全面的介紹了當前信息文本分類器現(xiàn)狀、國內(nèi)外分類器發(fā)展歷程,在分類特征提取、訓練樣本和眾多分類算法基礎上,從農(nóng)業(yè)信息文本分類特征項提取方式上從手,提出了具有農(nóng)業(yè)信息文本特色的特征提取方式,在此特征項訓練基礎上,建立農(nóng)業(yè)信息文本訓練庫,針對分類算法分類效果各有差異,使用改進優(yōu)化后的樸素貝葉斯分類器對農(nóng)業(yè)信息進行分類,設計實現(xiàn)了農(nóng)業(yè)信息搜索引擎分類器系統(tǒng)。世界上不會存在一模一樣的兩片葉子,每個對象都具有其獨特性,文本信息對象也都具有各自獨有識別特征以供識別分類。本文對文本特征提取四種方式信息增益、互信息、卡方統(tǒng)計和文檔頻率進行算法論述與實現(xiàn)實驗比較,提出農(nóng)業(yè)信息文本特征提取方式:基于文檔頻率的文本特征提取,將TF-IDF、空間向量模型與余弦相關度的計算運用其中,在此基礎上,依據(jù)農(nóng)業(yè)信息分類原則,根據(jù)識別度,選取各農(nóng)業(yè)類別的文本信息,最終建立了農(nóng)業(yè)信息文本訓練庫。任何一種分類算法都不具有絕對優(yōu)越性,都存在不同分類偏差,不同文本信息,分類器分類效果不一樣。本文實驗比較了決策樹算法、K-近鄰算法、支持向量機和樸素貝葉斯四種分類算法對農(nóng)業(yè)信息文本分類情況,運用并改進優(yōu)化樸素貝葉斯分類器,主要改進點兩個方面:樸素貝葉斯算法計算公式變化,將二值模型變換成多項式模型,建立多項式模型公式,進行實驗結果數(shù)據(jù)比較;在分類器部署方式上,將分類器分布式部署到多臺計算機,采用Top-N算法排序結果,進行實驗結果數(shù)據(jù)比較。本文根據(jù)多組分類實驗比較結果,在軟件設計理論上,結合上述改進優(yōu)化后樸素貝葉斯算法,使用農(nóng)業(yè)信息文本訓練庫,設計并實現(xiàn)了農(nóng)業(yè)信息搜索引擎分類器系統(tǒng),對農(nóng)業(yè)信息文本分類實驗測試得出結果數(shù)據(jù)。實驗結果表明,經(jīng)改進優(yōu)化后樸素貝葉斯分類器分類精度更高,分類速度更快,是實用可靠的農(nóng)業(yè)信息搜索引擎分類器系統(tǒng)。綜上,本文在農(nóng)業(yè)信息搜索引擎抓取農(nóng)業(yè)信息文本基礎上,從分類信息文本特征提取、農(nóng)業(yè)信息文本訓練、分類算法上對農(nóng)業(yè)信息文本分類器研究,通過實驗對比,提出農(nóng)業(yè)信息分類特征提取方式,建立農(nóng)業(yè)信息文本訓練庫,從算法上對樸素貝葉斯分類器改進,從部署上,將分類器系統(tǒng)分布式部署分類,最終達到改進優(yōu)化農(nóng)業(yè)信息文本分類器。本文為農(nóng)業(yè)信息文本分類提供了理論和基礎實驗平臺,同時,本文研究也可作為實際應用推廣應用。
[Abstract]:With the rapid development of the Internet, the human society has entered the era of information explosion, which brought a surge in agricultural knowledge and information network, brings convenience to the agricultural information search agricultural practitioners. Knowledge means wealth, the wealth of agricultural practitioners capture information from these agricultural information in agricultural knowledge however, massive information does not mean to the required information quickly and effectively, rapid positioning of agricultural information classification and search field refinement is necessary and necessary. Based on the agricultural information search engine classifier as the research object, comprehensively introduces the current information text classifier the status quo, development at home and abroad in the extraction of feature classification, classifier training samples and many classification algorithm based from the agricultural information, text classification feature extraction method from the hand of agricultural information with text feature extraction method in The characteristics of training on the basis of the establishment of agricultural information text training base, according to the classification results of different classification algorithms, for agricultural information classification using Naive Bayesian classifier improved after optimization, the design and implementation of agricultural information search engine system. The world does not exist classifier two leaves each object as like as two peas, has its unique characteristics also, the text information objects have their own unique feature for recognition and classification. In this paper, four kinds of text feature extraction, information gain, mutual information, chi square statistics and document frequency method is discussed and experimental comparison, put forward the feature extraction of text information extraction, text: agricultural characteristics based on document frequency TF-IDF. Vector space model and cosine calculation of correlation to use them on the basis of this, according to the principle of agricultural information classification, according to the degree of recognition, the selection of agricultural Industry categories of text information, finally established the agricultural information database. Any kind of training text classification algorithm has no absolute superiority, there are different classification bias, different text classification, the effects are not the same. This paper compared the decision tree algorithm, K- nearest neighbor algorithm, support vector machine and Naive Bayesian four classification the algorithm of text classification and use of agricultural information, optimization of Naive Bayesian classifier, the main improvement in two aspects: Naive Bayesian algorithm formula changes the value of the two models are transformed into polynomial model, a polynomial model formula, experimental results for data comparison; in the classifier deployment, the classifier distributed deployment to multiple computers the sequencing results, Top-N algorithm, the experimental results were compared. Based on the data sets classification experimental results, in the software design theory According to the above, the improved and optimized Naive Bayesian algorithm, the use of agricultural information text training base, the design and implementation of agricultural information search engine results of classifier system, data classification experiment of agricultural information text test. The experimental results show that the improved Subayers Park classifier has higher classification accuracy and faster classification speed is practical and reliable agricultural information search engine classification system. To sum up the search engine grab text based agricultural information in agricultural information, classified information extracted from text feature, text classification algorithm of agricultural information training, research on agricultural information text classifier, through the experimental comparison, put forward the agricultural information classification feature extraction method, the establishment of agricultural information text training base, improvement the Naive Bayesian classifier from the algorithm, from the deployment, the classifier system distributed classification, finally To improve the optimization of the agricultural information text classifier. This paper provides theoretical basis and experimental platform for agricultural information classification at the same time, this study can also be used as a practical application.

【學位授予單位】：東北農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP391.1

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本文編號：1653376