本文關鍵詞：基于多分類器集成的工業(yè)品缺陷分析方法研究　出處：《浙江大學》2017年碩士論文　論文類型：學位論文

  更多相關文章： 工業(yè)數(shù)據(jù) 代價敏感 多分類器 類別不平衡 集成方法

【摘要】：制造工業(yè)產品缺陷的分析是改進企業(yè)產品制造過程的重要途徑之一,對于產品質量以及營銷收益有著重要的研究意義和應用價值。伴隨計算機技術的快速發(fā)展、自動化系統(tǒng)的全面部署,產品制造過程中信息采集和存儲的難度大大降低。具有潛在信息和價值的數(shù)據(jù)在不斷地積累。同時,機器學習、數(shù)據(jù)挖掘等方法在各行各業(yè)取得了飛速的發(fā)展和應用。然而制造業(yè)由于工業(yè)性質對這些數(shù)據(jù)的利用水平遠不如其它行業(yè),并沒有真正地發(fā)揮這些數(shù)據(jù)應有的價值。為此,本文針對制造工業(yè)品數(shù)據(jù)的主要特點,總結了一般的針對工業(yè)產品缺陷分析問題的處理流程,對數(shù)據(jù)進行處理以及統(tǒng)計分析,并將分析產品各項質量檢測結果與產品的缺陷數(shù)據(jù)之間的關系問題,轉化成通過統(tǒng)計學習方法建立產品質量與缺陷的分類模型。然而缺陷數(shù)據(jù)同時出現(xiàn)多個缺陷類別以及類別樣本數(shù)目不平衡的問題,這對分類算法模型的構建而言是一大阻礙。本文針對需要同時掃清該兩者障礙提出了結合代價敏感與集成方法的多分類器模型,通過樣本重賦權重再縮放的方法結合分類代價敏感,再集成多個決策樹構建多分類模型。實驗結果表明該模型可以有效地處理不平衡類別的多分類問題,同時可以平衡分類代價和預測的準確率。此外對決策樹的集成擬合可以得出相關屬性的重要性度量,可以作為追溯缺陷主要影響因素的一個依據(jù)。
[Abstract]:Analysis of manufacturing industrial product defect is one of the most important ways to improve the enterprise production process, and has important research significance and application value for the quality of the products and marketing revenue. With the rapid development of computer technology, the full deployment of automation system, information collection and storage products in the manufacturing process has the potential to greatly reduce the difficulty of information and value. The data is accumulated ceaselessly. Machine learning method, at the same time, data mining has achieved rapid development and application in all walks of life. However, due to the nature of these manufacturing industry data use level is far behind that of other industries, and these data did not really play its due value. Therefore, this thesis mainly manufacture of industrial products the data, summed up the general on industrial product defect analysis processing procedures, data processing and statistical analysis, The analysis and the relationship between the detection results of defect data quality products, into learning classification model based on product quality and defects by statistic method. However, the defect data appear at the same time a number of samples and the type of defect category imbalance problem, which is a major impediment to the construction of classification model according to the need to clear away the obstacles. The two also proposed multi classifier model combining cost sensitive and integration method, through the method of sample weight to weight the combination of cost sensitive classification and zoom, and integration of multiple decision tree to construct multi classification model. The experimental results show that the model can effectively deal with unbalanced classes of multi classification problems. At the same time can balance the cost of classification accuracy and prediction. In addition to measure the importance of integrated fitting decision tree can draw relevant attributes, can As a basis for the main influencing factors of retroactive defects.

【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

【參考文獻】

相關期刊論文 前1條

1 周志華,陳世福;神經網(wǎng)絡集成[J];計算機學報;2002年01期

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本文編號：1425246