本文選題：特征選擇 + 遺傳算法��； 參考：《河北大學(xué)》2017年碩士論文

【摘要】：特征選擇是指從初始特征全集中,依據(jù)既定規(guī)則篩選出特征子集的過(guò)程。通過(guò)剔除冗余特征,以達(dá)到降低算法復(fù)雜度和提高算法性能的目的。特征選擇是解決維數(shù)災(zāi)難問(wèn)題的有效手段,在機(jī)器學(xué)習(xí)中扮演著重要角色。研究特征選擇具有重要的理論及應(yīng)用價(jià)值,特別是對(duì)于大數(shù)據(jù)時(shí)代的機(jī)器學(xué)習(xí)。本文在離散值特征選擇問(wèn)題上,提出了兩種不同的基于進(jìn)化算法的特征選擇方法。第一種方法用相對(duì)分類(lèi)信息熵作為適應(yīng)度函數(shù),度量特征子集的重要性,理論證明了這種度量的可行性,用進(jìn)化算法(遺傳算法、粒子群算法)尋找最優(yōu)特征子集。第二種方法和第一種方法類(lèi)似,不同的是用不一致率作為適應(yīng)度函數(shù),度量特征子集的重要性。本文通過(guò)比較研究這兩種方法,得到了如下結(jié)論:(a)當(dāng)采用相同的適應(yīng)度函數(shù)時(shí),用粒子群搜索最優(yōu)特征子集與用遺傳算法搜索最優(yōu)特征子集相比,前者在測(cè)試精度和收斂速度兩方面均優(yōu)于后者。(b)當(dāng)采用不同的適應(yīng)度函數(shù)時(shí),選擇相對(duì)分類(lèi)信息熵作為適應(yīng)度函數(shù)的進(jìn)化特征選擇方法要優(yōu)于選擇不一致率作為適應(yīng)度函數(shù)的進(jìn)化特征選擇方法。另外,論文還研究了本文提出的算法在連續(xù)值情況下的推廣。本文提出的算法具有三個(gè)特點(diǎn):(1)簡(jiǎn)單且易于實(shí)現(xiàn);(2)特征子集表示能力較強(qiáng);(3)具有好的語(yǔ)義可解釋性。
[Abstract]:Feature selection refers to the process of screening feature subset according to the established rules in the complete set of features. By eliminating redundant features, it can reduce the complexity of the algorithm and improve the performance of the algorithm. Feature selection is an effective means to solve the problem of dimension disaster and plays an important role in machine learning. The important theory and application value, especially for machine learning in the era of large data. In this paper, two different feature selection methods based on evolutionary algorithms are proposed in the selection of discrete value features. The first method uses the relative information entropy as the fitness function to measure the importance of the feature subset, and the theory proves this degree. The feasibility of using the evolutionary algorithm (genetic algorithm, particle swarm optimization) to find the best special subset. The second method is similar to the first one, and the difference is to use the inconsistency as the fitness function to measure the importance of the feature subset. In this paper, the following conclusions are obtained by comparing the two methods: (a) when the same fitness is used Compared with the genetic algorithm, the former is superior to the latter in two aspects of testing precision and convergence speed. (b) when different fitness functions are used, the selection method of selecting the relative entropy as the fitness function is better than the choice of the inconsistency rate. As an evolutionary feature selection method for fitness function, the paper also studies the generalization of the algorithm proposed in this paper in the case of continuous values. The proposed algorithm has three characteristics: (1) simple and easy to implement; (2) the ability to express the feature subset is stronger; (3) it has good semantic interpretability.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

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