本文關(guān)鍵詞： 樹葉分類 支持向量機(jī) 粒子群算法 主成分分析法 癌癥分類 卷積神經(jīng)網(wǎng)絡(luò)　出處：《中北大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)實(shí)中的很多實(shí)際問題都可以轉(zhuǎn)化為數(shù)據(jù)信息處理中的數(shù)據(jù)分類問題,例如氣象預(yù)報(bào)、商品推薦、生物信息、網(wǎng)絡(luò)檢測(cè)等,而數(shù)據(jù)信息處理都是以機(jī)器學(xué)習(xí)為基礎(chǔ)進(jìn)行研究的。隨著科學(xué)技術(shù)的發(fā)展,機(jī)器學(xué)習(xí)算法的應(yīng)用領(lǐng)域也變得十分廣泛。本文主要介紹了兩種機(jī)器學(xué)習(xí)算法:粒子群算法優(yōu)化支持向量機(jī)和卷積神經(jīng)網(wǎng)絡(luò)。其中研究了粒子群算法優(yōu)化支持向量機(jī)在樹葉分類和癌癥基因分類中的預(yù)測(cè),卷積神經(jīng)網(wǎng)絡(luò)在圖像分類中的應(yīng)用。(1)基于各種樹葉的特征構(gòu)建一個(gè)數(shù)據(jù)預(yù)處理模型:先對(duì)各種數(shù)據(jù)進(jìn)行歸一化處理,采用主成分分析方法從16個(gè)特征中提取出3個(gè)主成分,再建立粒子群算法優(yōu)化后的支持向量機(jī),用支持向量機(jī)對(duì)樹葉數(shù)據(jù)進(jìn)行分類預(yù)測(cè)。實(shí)驗(yàn)結(jié)果表明,相對(duì)于遺傳算法和網(wǎng)格搜索法尋到的最優(yōu)參數(shù)相比,粒子群算法優(yōu)化支持向量機(jī)具有最高的準(zhǔn)確率,高達(dá)94.1%,高于其他兩種分類方法。(2)將粒子群優(yōu)化的支持向量機(jī)模型應(yīng)用到癌癥基因分類中,通過選取多組不同的實(shí)驗(yàn)數(shù)據(jù)對(duì)癌癥手術(shù)后病人的復(fù)發(fā)和不復(fù)發(fā)的基因樣本進(jìn)行預(yù)測(cè)分類。對(duì)于三種不同分類方法對(duì)于癌癥基因分類的不同分類效果,綜合實(shí)驗(yàn)結(jié)果,粒子群優(yōu)化支持向量機(jī)在三種分類方法中達(dá)到最好的分類效果。(3)將卷積神經(jīng)網(wǎng)絡(luò)應(yīng)用到圖像處理上,通過優(yōu)化卷積神經(jīng)網(wǎng)絡(luò)卷積層和池化層中的濾波器函數(shù),達(dá)到了優(yōu)化性能的作用,再構(gòu)造一定結(jié)構(gòu)的卷積神經(jīng)網(wǎng)絡(luò),然后將該模型對(duì)圖像數(shù)據(jù)集進(jìn)行分類處理,在對(duì)圖像進(jìn)行最后達(dá)到預(yù)期的分類結(jié)果。
[Abstract]:Many practical problems in reality can be transformed into data classification problems in data information processing, such as weather forecast, commodity recommendation, biological information, network detection, etc. And data processing is based on machine learning. With the development of science and technology, In this paper, we mainly introduce two kinds of machine learning algorithms: particle swarm optimization support vector machine and convolution neural network. The prediction of the measuring machine in leaf classification and cancer gene classification, The application of convolution neural network in image classification. (1) A data preprocessing model is constructed based on the characteristics of various leaves. Firstly, the data are normalized, and three principal components are extracted from 16 features by principal component analysis (PCA). Finally, the support vector machine (SVM) is established, which can be used to classify and predict the leaf data. The experimental results show that compared with the optimal parameters obtained by genetic algorithm and grid search, Particle swarm optimization support vector machine (SVM) has the highest accuracy, up to 94. 1%, which is higher than the other two classification methods. (2) the particle swarm optimization support vector machine model is applied to cancer gene classification. By selecting different groups of experimental data to predict and classify the recurrence and non-recurrence gene samples of patients with cancer after operation, three different classification methods for different classification effects of cancer gene classification were synthesized. Particle swarm optimization support vector machine achieves the best classification effect in three classification methods. The convolution neural network is applied to image processing. The filter functions in convolution layer and pool layer are optimized. The function of optimizing performance is achieved, and a convolution neural network with certain structure is constructed, then the image data set is classified by the model, and the expected classification result is achieved at the end of the image classification.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18;TP391.41

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