本文選題：模式識別 + 微陣列數(shù)據(jù)分類��； 參考：《電子學(xué)報》2016年10期

【摘要】：針對傳統(tǒng)模式識別方法在學(xué)習(xí)具有小樣本特性的DNA微陣列數(shù)據(jù)時存在的過擬合問題,本文提出了一種子空間融合演化超網(wǎng)絡(luò)模型.該模型通過子空間劃分、超邊全覆蓋和子空間融合三種方法降低模型對初始化的依賴,減少了對數(shù)據(jù)空間的擬合誤差,提高了演化超網(wǎng)絡(luò)的泛化能力.對四個DNA微陣列數(shù)據(jù)集的實驗結(jié)果表明,子空間融合演化超網(wǎng)絡(luò)的識別率和在小樣本訓(xùn)練集下的泛化能力均優(yōu)于參與對比的其他傳統(tǒng)模式識別方法.
[Abstract]:Aiming at the over-fitting problem of traditional pattern recognition methods in learning DNA microarray data with small sample characteristics, a subspace fusion evolutionary supernetwork model is proposed in this paper. The model reduces the dependence on initialization, reduces the fitting error of data space and improves the generalization ability of evolutionary supernetwork by subspace partitioning, hyper-edge full coverage and subspace fusion. The experimental results of four DNA microarray datasets show that the recognition rate of the subspace fusion evolution supernetwork and the generalization ability under the small sample training set are superior to those of other traditional pattern recognition methods involved in the comparison.
【作者單位】： 重慶郵電大學(xué)計算智能重慶市重點(diǎn)實驗室;
【基金】：國家自然科學(xué)基金(No.61203308,No.61403054) 重慶教委科學(xué)技術(shù)研究項目(自然科學(xué)類)(No.KJ1400436) 重慶市基礎(chǔ)與前沿研究計劃項目(No.cstc2014jcyj A40001)
【分類號】：R440;O157.5
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本文編號：1801854