發(fā)布時(shí)間：2018-01-04 13:29

  本文關(guān)鍵詞：基于決策粗糙集模型的多類代價(jià)敏感學(xué)習(xí)研究　出處：《南京理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 決策粗糙集 三支決策 代價(jià)敏感學(xué)習(xí) 多類問題 多階段分類 文本分類

【摘要】：近年來,隨著計(jì)算機(jī)和互聯(lián)網(wǎng)技術(shù)的發(fā)展與普及,產(chǎn)生的海量數(shù)據(jù)信息除了在規(guī)模上、復(fù)雜性上和過去的信息有很大不同之外,還存在著一定的不確定性和模糊性。決策粗糙集模型作為一種處理不精確、不確定性問題的方法,通過引入貝葉斯決策過程,給出了根據(jù)損失函數(shù)矩陣計(jì)算決策閾值的系統(tǒng)化方法,進(jìn)而得到基于粗糙集三個(gè)區(qū)域的三支決策框架,很好的解決了用戶在信息不足時(shí)如何做出合理決策的問題。現(xiàn)有的很多工作都是基于經(jīng)典二類決策粗糙集模型展開的,對(duì)于多分類問題,大多是將其轉(zhuǎn)化為多個(gè)二分類問題進(jìn)而用二類決策粗糙集分類方法進(jìn)行處理,這既要求用戶提供更多的損失函數(shù),又降低了計(jì)算的效率。鑒于此,本文將決策粗糙集與代價(jià)敏感學(xué)習(xí)相結(jié)合,提出了一種多類決策粗糙集模型,并以此模型為基礎(chǔ)對(duì)代價(jià)敏感學(xué)習(xí)進(jìn)行了研究,主要包括如下的研究內(nèi)容:第一,決策粗糙集在分類模型上的擴(kuò)展。損失函數(shù)矩陣在決策粗糙集中具有重要作用,在二分類問題中,可以很容易的根據(jù)損失函數(shù)矩陣計(jì)算出決策閾值。本文從語義角度出發(fā),以損失函數(shù)為研究對(duì)象,將決策粗糙集與代價(jià)敏感學(xué)習(xí)相結(jié)合,利用經(jīng)典代價(jià)敏感學(xué)習(xí)提供的多類問題代價(jià)矩陣推導(dǎo)出多類情況下的損失函數(shù)值,進(jìn)而提出多類決策粗糙集模型,并基于此模型設(shè)計(jì)代價(jià)敏感三支決策分類算法,通過對(duì)比實(shí)驗(yàn)結(jié)果分析說明了所提算法在處理多類代價(jià)敏感問題中的有效性。第二,基于多類決策粗糙集模型的多階段代價(jià)敏感學(xué)習(xí)方法。決策粗糙集的分類方法輸出的是三支決策結(jié)果,只有劃分到正域的對(duì)象才能以高置信度確定其類標(biāo),而劃分到邊界域的對(duì)象因信息不足而做出延遲決策,劃分到負(fù)域中的對(duì)象因置信度不高而做出拒絕決策,即這兩個(gè)區(qū)域中的對(duì)象在三支決策結(jié)果中都未給定具體類標(biāo)。針對(duì)此問題,本文提出了基于多類決策粗糙集模型的多階段代價(jià)敏感學(xué)習(xí)方法,通過多個(gè)階段的分類過程以最終消除邊界域和負(fù)域,將三支決策分類轉(zhuǎn)化為二支決策分類。實(shí)驗(yàn)結(jié)果表明了所提算法具有較好的分類性能。第三,基于多類決策粗糙集模型的文本分類。文本分類是近年來的研究熱點(diǎn),本文以搜狗中文文本為語料庫,使用基于多類決策粗糙集模型的多階段代價(jià)敏感學(xué)習(xí)算法訓(xùn)練文本分類器,并與幾種常用的機(jī)器學(xué)習(xí)分類算法進(jìn)行實(shí)驗(yàn)對(duì)比,結(jié)果表明本文的方法在文本分類中具有更高的分類精確率和召回率以及更低的分類代價(jià),進(jìn)一步突出本文所提算法的代價(jià)敏感性并擴(kuò)展了決策粗糙集在實(shí)際問題中的應(yīng)用。
[Abstract]:In recent years, with the development of computer and Internet technology and the popularity of the massive data except in size, complexity and past information are very different, there are still some uncertainty and fuzziness. The decision model of rough sets as a method to deal with imprecise, uncertain problems, through the introduction of Bayesian decision process, gives a systematic method to calculate the decision threshold according to the loss function of the matrix, and then obtain the rough set three decision framework of three regions based on a good solution to the user how to make a reasonable decision in the information shortage problem. Many existing works are classic two kinds of rough set model based on decision making for the multi classification problem, is transformed into a plurality of two classification problems and two kinds of decision rough set classification method for processing, it is required to provide users with more The loss of function, and reduces the computing efficiency. In view of this, this paper will be combined with rough set decision and cost sensitive learning, proposes a multi class decision rough set model, and based on the model of cost sensitive learning, including the main research contents are as follows: first, extended rough set decision in the classification model. The loss function matrix in rough set decision-making plays an important role in the two classification problems, can be easily calculated according to the loss function matrix decision threshold. In this paper, from the semantic angle, on the loss function as the research object, combined with rough set decision and cost sensitive learning, using sensitive learning the price of the classic multi class problem cost matrix to derive the loss function of the multi class case, and then put forward the multi class decision rough set model, and based on this model design cost sensitive three decision points Algorithms, by comparing the experimental results demonstrate that the proposed algorithm is effective in dealing with the multi class cost sensitive problems. Second, multi stage cost sensitive learning method for multi class decision based on rough sets model. The output classification method of rough set decision is the three decision results, only to the positive region of the object can be divided with high confidence to determine their standard, and divided into object boundary region due to the lack of information and make a decision to delay, division of objects in the domain of the negative because of confidence is not high but refused to make decisions, namely the two areas of the objects in the result are not given specific classes in three. According to the standard decision this problem, this paper proposes a multi stage cost sensitive learning method for multi class decision based on rough sets model, through the classification process of multiple stages in order to eliminate the boundary region and negative region will eventually support three decision classification into two branch classification. The experiment results show that the proposed algorithm has better classification performance. Third, multi class text classification decision based on rough sets model. Text classification is a hot research topic in recent years, with the Chinese Sogou text corpus, the use of multi class decision based on rough sets model generation cost sensitive learning algorithm to train classifier, and and several commonly used machine learning classification algorithm by experiments, results show that the classification cost of this method has higher classification in text classification precision rate and recall rate and lower, further highlight the proposed cost sensitive algorithm and extended the application of rough set decision in the actual problem.

【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18

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