本文選題：農(nóng)業(yè)信息分類 + 深度學(xué)習(xí)��； 參考：《西北農(nóng)林科技大學(xué)》2017年碩士論文

【摘要】：隨著國家對農(nóng)業(yè)的大力扶持以及互聯(lián)網(wǎng)技術(shù)的迅猛發(fā)展,農(nóng)業(yè)相關(guān)信息不斷地膨脹擴(kuò)大,農(nóng)業(yè)信息化發(fā)展迅速,在線農(nóng)業(yè)信息已經(jīng)海量化。如何從海量化的農(nóng)業(yè)信息中實現(xiàn)農(nóng)業(yè)信息的快速搜索和準(zhǔn)確定位已經(jīng)變得越來越困難。在這樣的背景下,選擇優(yōu)化的農(nóng)業(yè)信息分類方法,輔助實現(xiàn)農(nóng)業(yè)信息的快速檢索、準(zhǔn)確定位是至關(guān)重要的。本文對基于決策樹、貝葉斯和深度學(xué)習(xí)的農(nóng)業(yè)信息分類方法進(jìn)行了研究。重點探討了深度學(xué)習(xí)中的卷積神經(jīng)網(wǎng)絡(luò)的網(wǎng)絡(luò)結(jié)構(gòu)和網(wǎng)絡(luò)訓(xùn)練過程,實現(xiàn)了對農(nóng)業(yè)信息的自動分類,提高了文本分類的精度和效率,來增加信息的利用價值。主要工作如下:(1)數(shù)據(jù)獲取及預(yù)處理部分。利用爬蟲程序從中國農(nóng)業(yè)信息網(wǎng)上獲得相關(guān)欄目下的文檔作為農(nóng)業(yè)信息數(shù)據(jù)集,然后利用Jieba分詞和Pynlpir兩種分詞方法對數(shù)據(jù)集進(jìn)行分詞處理,并利用停頓詞表去除分詞文件中的符號、數(shù)字等一些不能代表文本特征的無用詞匯,接著運用常用的特征選擇評價函數(shù)進(jìn)行特征選擇,在此基礎(chǔ)上證明了利用卷積神經(jīng)網(wǎng)絡(luò)自動提取農(nóng)業(yè)信息特征的可行性。(2)農(nóng)業(yè)信息的兩種向量化表示方法。一種是中文分詞、去停頓詞后抽取文本特征詞然后表示成文本向量方法;一種是中文分詞、去停頓詞后直接表示成詞向量方法;利用詞向量的方法避免了傳統(tǒng)向量表示維數(shù)過高的問題,利用深度學(xué)習(xí)的方法可以自動提取農(nóng)業(yè)信息的特征詞。(3)基于預(yù)處理生成的向量文件,分別利用決策樹、貝葉斯和深度學(xué)習(xí)的卷積神經(jīng)網(wǎng)絡(luò)模型實現(xiàn)了農(nóng)業(yè)信息分類,并對運行結(jié)果進(jìn)行了理論分析,針對二分類與十分類的運行結(jié)果差異提出了思考,接著運用聚類的方法驗證了數(shù)據(jù)集類別文本的分布情況并利用餅狀圖直觀顯示,從而驗證二分類和十分類運行結(jié)果的差異是因為數(shù)據(jù)集各類別文檔數(shù)目不平衡造成的。通過實驗驗證了卷積神經(jīng)網(wǎng)絡(luò)應(yīng)用于農(nóng)業(yè)信息分類問題上的可行性,并與其他現(xiàn)有的分類器進(jìn)行比較,分析了卷積神經(jīng)網(wǎng)絡(luò)在農(nóng)業(yè)信息分類上的優(yōu)越性。(4)針對農(nóng)業(yè)信息分類的卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)提出了優(yōu)化思考,對實驗結(jié)果進(jìn)行了理論對比分析。結(jié)果表明,針對農(nóng)業(yè)信息分類的網(wǎng)絡(luò)結(jié)構(gòu)中各節(jié)點均采用Sigmoid激勵函數(shù)時網(wǎng)絡(luò)分類性能下降明顯,而各節(jié)點均采用Relu激勵函數(shù)時網(wǎng)絡(luò)分類性能顯著提高。在調(diào)整卷積核個數(shù)實驗中,增多網(wǎng)絡(luò)模型中卷積核的個數(shù)到原來的兩倍,網(wǎng)絡(luò)最終達(dá)到了99.40%的分類精確率。
[Abstract]:With the strong support of agriculture and the rapid development of Internet technology, agricultural information has been expanding and expanding, agricultural information has developed rapidly, and the online agricultural information has become massive. It has become more and more difficult to realize the rapid search and accurate positioning of agricultural information from mass agricultural information. In the background, it is very important to select the optimized classification method of agricultural information and assist in the rapid retrieval of agricultural information and accurate positioning. This paper studies the classification method of agricultural information based on decision tree, Bias and deep learning. The network structure and network training of convolution neural network in deep learning are discussed in this paper. It realizes the automatic classification of agricultural information, improves the accuracy and efficiency of text classification to increase the use value of information. The main work is as follows: (1) data acquisition and preprocessing parts. Using the crawler program to obtain the documents under the related columns from the Chinese agricultural information network as the agricultural information data set, and then use the Jieba participle and the Py Nlpir two participle methods are used to divide the data sets, and use the pause word list to remove the symbols in the participle files, numbers and other useless words that can not represent the text features. Then use the common features to select the evaluation function for feature selection. On this basis, it is proved that the agricultural information is automatically extracted by the convolution neural network. (2) two quantitative representation methods of agricultural information. One is Chinese participle, the text feature words are extracted after the pause words are extracted and then expressed as text vector methods; one is Chinese participle, the word vector method is directly expressed after the pause word, and the method of word vector is used to avoid the problem that the dimension of traditional vector expression is too high. The characteristic words of agricultural information can be automatically extracted by means of deep learning. (3) based on the vector files generated by preprocessing, the agricultural information classification is realized by using the decision tree, Bias and the convolution neural network model of deep learning, and the operation results are analyzed in theory, and the difference between the two classification and the very class operation results is different. The thinking is put forward, and then the clustering method is used to verify the distribution of the text of the dataset and use the pie chart to display it intuitively, thus verifying that the difference between the two classification and the very class operation results is caused by the imbalance of the number of documents in the data sets. The application of convolution neural network to the classification of agricultural information is verified by experiments. The feasibility of the problem is compared with the other existing classifiers and the superiority of convolution neural network in the classification of agricultural information is analyzed. (4) the optimization thinking is put forward for the convolution neural network structure of agricultural information classification, and the experimental results are compared and analyzed. The results show that the network node for the classification of agricultural information has been shown. The network classification performance decreases obviously when each node uses the Sigmoid excitation function, while the network classification performance is significantly improved when each node uses the Relu excitation function. The number of convolution kernel in the increased network model is two times that of the original convolution kernel, and the network reaches 99.40% classification accuracy at the end of the network.

【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S126

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