基于嶺估計和AMOGA的馬田系統(tǒng)分類方法
發(fā)布時間:2018-11-18 09:50
【摘要】:馬田系統(tǒng)是多變量數(shù)據(jù)挖掘中模式識別方法的新進展,變量間的復(fù)共線性會通過馬氏距離影響馬田系統(tǒng)變量篩選的效果和判別的準(zhǔn)確率。為了克服復(fù)共線性對馬田系統(tǒng)的負(fù)面影響,提出了基于嶺估計新的測量尺度—嶺馬氏距離,通過變量敏感性和條件數(shù)繪制三條嶺跡來確定嶺參數(shù),并設(shè)計了自適應(yīng)多目標(biāo)遺傳算法進行基準(zhǔn)空間優(yōu)化,使得馬田系統(tǒng)分類方法對病態(tài)數(shù)據(jù)具有更好的耐受性。通過案例驗證了嶺馬氏距離可以很好的克服復(fù)共線性,并提高馬田系統(tǒng)分類方法的判別準(zhǔn)確率。
[Abstract]:Multi field system is a new development of pattern recognition method in multivariate data mining. The complex collinearity between variables will affect the effect of variable selection and the accuracy of discriminating through Markov distance. In order to overcome the negative influence of complex collinearity on the Matton system, a new measurement scale of Ridge estimation, called Ridge distance, is proposed. The ridge parameters are determined by mapping three ridge traces by variable sensitivity and conditional number. The adaptive multi-objective genetic algorithm is designed to optimize the reference space, which makes the classification method of Matian system more tolerant to ill-conditioned data. It is proved by a case study that Lingmao distance can overcome the complex collinearity and improve the accuracy of the classification method.
【作者單位】: 南京理工大學(xué)經(jīng)濟管理學(xué)院;江蘇科技大學(xué)經(jīng)濟管理學(xué)院;國立臺北商業(yè)大學(xué)管理學(xué)院;
【基金】:國家自然科學(xué)基金資助項目(71271114) 教育部人文社會科學(xué)基金資助項目(14YJA910004)
【分類號】:TP18;TP311.13
本文編號:2339707
[Abstract]:Multi field system is a new development of pattern recognition method in multivariate data mining. The complex collinearity between variables will affect the effect of variable selection and the accuracy of discriminating through Markov distance. In order to overcome the negative influence of complex collinearity on the Matton system, a new measurement scale of Ridge estimation, called Ridge distance, is proposed. The ridge parameters are determined by mapping three ridge traces by variable sensitivity and conditional number. The adaptive multi-objective genetic algorithm is designed to optimize the reference space, which makes the classification method of Matian system more tolerant to ill-conditioned data. It is proved by a case study that Lingmao distance can overcome the complex collinearity and improve the accuracy of the classification method.
【作者單位】: 南京理工大學(xué)經(jīng)濟管理學(xué)院;江蘇科技大學(xué)經(jīng)濟管理學(xué)院;國立臺北商業(yè)大學(xué)管理學(xué)院;
【基金】:國家自然科學(xué)基金資助項目(71271114) 教育部人文社會科學(xué)基金資助項目(14YJA910004)
【分類號】:TP18;TP311.13
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