基于布谷鳥算法的特征選擇研究
發(fā)布時間：2019-01-27 22:43
【摘要】：機器學(xué)習(xí)是目前最熱門的領(lǐng)域之一,吸引了來自社會各界的研究和目光。它不僅已經(jīng)在醫(yī)療、圖像、文本等領(lǐng)域得到了應(yīng)用,而且正在作為一支生力軍保持著其旺盛發(fā)展的勢頭。機器學(xué)習(xí)是以數(shù)據(jù)為驅(qū)動的,而現(xiàn)實中的許多問題往往涉及到的可能的影響因素有很多,這就使得原始特征具有高維特性。這種特性對于機器學(xué)習(xí)來說,不僅訓(xùn)練及預(yù)測效率低,而且還可能對無關(guān)、冗余特征敏感,導(dǎo)致精度降低。特征選擇是一種有效解決上述問題的方法。在本文中,將首先對特征預(yù)處理和特征選擇的流程進行介紹,然后再從搜索策略和評價準則兩個角度對特征選擇進行分類,并對其中的方法進行闡述。選擇支持向量機作為評價特征選擇結(jié)果優(yōu)劣的標準,對其中的原理、推導(dǎo)、核的概念還有泛化方法給出介紹。粒子群算法和布谷鳥算法可以作為一種隨機策略應(yīng)用在特征選擇上,所以本文將對兩種算法的原理、方法進行敘述,并根據(jù)具體需要引出它們的二進制版本。本文在對二進制布谷鳥算法的搜索行為進行分析以后,提出了一種新的二進制布谷鳥算法,在這種算法中會對搜索方向和搜索精度上進行優(yōu)化。在此基礎(chǔ)上提出了PSO-NCS算法,將改進的二進制布谷鳥算法與PSO算法結(jié)合,充分利用了PSO的收斂的聚集特性和CS的全局尋優(yōu)能力,使兩者優(yōu)勢互補,不僅能跳出局部最優(yōu),還能有更好的收斂精度。使用PSO-NCS算法對幾種數(shù)據(jù)集進行實驗,得到的結(jié)果略優(yōu)于其他算法,被證明在特征選擇問題上,是一種尋優(yōu)能力更強、收斂速度更快的算法。
[Abstract]:Machine learning is one of the most popular fields, attracting research and attention from all walks of life. It not only has been applied in medical treatment, image, text and other fields, but also is maintaining its vigorous development as a new force. Machine learning is data-driven, but many problems in reality often involve a lot of possible factors, which makes the original features have high-dimensional characteristics. This characteristic is not only low in training and prediction efficiency, but also sensitive to irrelevant and redundant features for machine learning, resulting in a decrease in accuracy. Feature selection is an effective method to solve the above problems. In this paper, we first introduce the process of feature preprocessing and feature selection, and then classify feature selection from two aspects of search strategy and evaluation criterion, and explain the methods. Support vector machine (SVM) is chosen as the criterion to evaluate the result of feature selection. The principle, derivation, kernel concept and generalization method are introduced. Particle swarm optimization and cuckoo algorithm can be applied to feature selection as a random strategy, so this paper will describe the principle and methods of the two algorithms, and draw out their binary versions according to the specific needs. After analyzing the search behavior of binary cuckoo algorithm, a new binary cuckoo algorithm is proposed, in which the search direction and precision are optimized. On this basis, a new PSO-NCS algorithm is proposed, which combines the improved binary cuckoo algorithm with the PSO algorithm, and makes full use of the convergence characteristics of PSO and the global optimization ability of CS, which makes both of them complement each other and can not only jump out of the local optimum. Better convergence accuracy can also be achieved. The PSO-NCS algorithm is used to test several data sets, and the result is slightly better than other algorithms. It is proved that the algorithm is more efficient and convergent in feature selection.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18
本文編號：2416775
[Abstract]:Machine learning is one of the most popular fields, attracting research and attention from all walks of life. It not only has been applied in medical treatment, image, text and other fields, but also is maintaining its vigorous development as a new force. Machine learning is data-driven, but many problems in reality often involve a lot of possible factors, which makes the original features have high-dimensional characteristics. This characteristic is not only low in training and prediction efficiency, but also sensitive to irrelevant and redundant features for machine learning, resulting in a decrease in accuracy. Feature selection is an effective method to solve the above problems. In this paper, we first introduce the process of feature preprocessing and feature selection, and then classify feature selection from two aspects of search strategy and evaluation criterion, and explain the methods. Support vector machine (SVM) is chosen as the criterion to evaluate the result of feature selection. The principle, derivation, kernel concept and generalization method are introduced. Particle swarm optimization and cuckoo algorithm can be applied to feature selection as a random strategy, so this paper will describe the principle and methods of the two algorithms, and draw out their binary versions according to the specific needs. After analyzing the search behavior of binary cuckoo algorithm, a new binary cuckoo algorithm is proposed, in which the search direction and precision are optimized. On this basis, a new PSO-NCS algorithm is proposed, which combines the improved binary cuckoo algorithm with the PSO algorithm, and makes full use of the convergence characteristics of PSO and the global optimization ability of CS, which makes both of them complement each other and can not only jump out of the local optimum. Better convergence accuracy can also be achieved. The PSO-NCS algorithm is used to test several data sets, and the result is slightly better than other algorithms. It is proved that the algorithm is more efficient and convergent in feature selection.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18
【參考文獻】
相關(guān)期刊論文 前7條
1 李東生;高楊;雍愛霞;;基于改進離散布谷鳥算法的干擾資源分配研究[J];電子與信息學(xué)報;2016年04期
2 張晶;吳虎勝;;改進二進制布谷鳥搜索算法求解多維背包問題[J];計算機應(yīng)用;2015年01期
3 馮登科;阮奇;杜利敏;;二進制布谷鳥搜索算法[J];計算機應(yīng)用;2013年06期
4 高潮;劉志雄;;基于輪盤賭編碼和粒子群算法的并行機調(diào)度優(yōu)化[J];機械制造;2010年06期
5 陳濤;張思發(fā);;分支限界法求解實際TSP問題[J];計算機工程與設(shè)計;2009年10期
6 王輝;錢鋒;;群體智能優(yōu)化算法[J];化工自動化及儀表;2007年05期
7 常彥偉;王耀才;曹云峰;王致杰;;基于誤差相關(guān)度學(xué)習(xí)樣本選擇[J];計算機工程與設(shè)計;2007年16期
相關(guān)博士學(xué)位論文 前2條
1 黃東山;特征選擇及半監(jiān)督分類方法研究[D];華中科技大學(xué);2011年
2 劉建華;粒子群算法的基本理論及其改進研究[D];中南大學(xué);2009年
相關(guān)碩士學(xué)位論文 前1條
1 黃繼達;布谷鳥算法的改進及其應(yīng)用研究[D];華中科技大學(xué);2014年
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