本文選題：特征選擇 + 模糊集��； 參考：《大連海事大學》2017年碩士論文

【摘要】：特征選擇是一種用來降低數(shù)據(jù)集維度的技術,其核心是從輸入的特征集合中選擇出最具有預測性的特征子集來代表原始數(shù)據(jù)集合。特征選擇不僅可以簡化特征內(nèi)在的關系還可以改善整體集合的預測能力。目前,許多學者針對模糊粗糙集的特征選擇進行了大量的研究,其中比較常見的有,遺傳算法、蟻群算法(ACO)、粒子群算法(PSO)等。這些算法在魯棒性和求解能力等方面均表現(xiàn)優(yōu)秀,并且它們的共同特點是只有最優(yōu)秀的個體才能有機會被提取出來。然而,某些初始依賴度值低的個體有可能帶有重要信息,因此上述這些算法可能會導致重要信息丟失。針對以上問題,本文研究了野草算法的特點并且發(fā)現(xiàn)其特點能使模糊粗糙特征選擇更加全面。野草算法認為初始依賴度值低的個體有可能帶有重要信息,因而賦予初始依賴度值低的個體一定的存活機會。該算法早期能夠維持特征種群多樣性而在后期能夠保證最優(yōu)解的選擇優(yōu)勢。因此,本文首先將野草算法的特點和模糊粗糙集理論相結(jié)合,繼而提出了基于優(yōu)化野草算法的加權模糊粗糙特征選擇算法并對其進行編程實現(xiàn)。其次,利用基于模糊粗糙集的快速屬性約簡算法來驗證特征選擇結(jié)果。最后,將算法模型應用于十四類基準數(shù)據(jù)集和四類具有現(xiàn)實背景意義的乳腺造影數(shù)據(jù)集進行特征選擇,并且將本文算法的特征選擇結(jié)果與其他兩個算法(蟻群算法和粒子群算法)的特征選擇結(jié)果分別從分類精度和AUC值兩個方面做出對比分析。數(shù)據(jù)分析結(jié)果表明,基于本文算法得到的大部分特征選擇結(jié)果可以很好地代表原始數(shù)據(jù)集并且整體性能優(yōu)于蟻群算法和粒子群算法。同時,這也證明了本文算法具有現(xiàn)實研究意義。
[Abstract]:Feature selection is a technique to reduce the dimension of data set. The core of feature selection is to select the most predictive feature subset from the input feature set to represent the original data set. Feature selection not only simplifies the intrinsic relationship of features, but also improves the prediction ability of the whole set. At present, many scholars have done a lot of research on the feature selection of fuzzy rough sets, among which the common ones are genetic algorithm, ant colony algorithm (ACOO), particle swarm optimization (PSO) and so on. These algorithms are excellent in robustness and solving ability, and their common feature is that only the best individual can be extracted. However, some individuals with low initial dependency may have important information, so these algorithms may lead to the loss of important information. In view of the above problems, this paper studies the characteristics of the weed algorithm and finds that its characteristics can make the fuzzy rough feature selection more comprehensive. The weed algorithm considers that the individuals with low initial dependency may have important information, so the individuals with low initial dependency are given a certain chance of survival. The algorithm can maintain the diversity of characteristic populations in the early stage and ensure the selection advantage of the optimal solution in the later stage. Therefore, this paper first combines the characteristics of weed algorithm with fuzzy rough set theory, and then proposes a weighted fuzzy rough feature selection algorithm based on optimal weed algorithm and implements it by programming. Secondly, the fast attribute reduction algorithm based on fuzzy rough set is used to verify the feature selection results. Finally, the algorithm model is applied to 14 kinds of datum data sets and 4 kinds of mammography data sets with realistic background significance for feature selection. The result of feature selection of this algorithm is compared with that of other two algorithms (ant colony algorithm and particle swarm optimization algorithm) from classification accuracy and AUC value. The results of data analysis show that most of the feature selection results based on this algorithm can represent the original data set well and the overall performance is better than that of ant colony algorithm and particle swarm optimization algorithm. At the same time, it also proves that this algorithm has practical significance.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP18

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本文編號：1827003