本文選題：偏最小二乘回歸 + k近鄰�。� 參考：《計算機(jī)應(yīng)用》2017年03期

【摘要】：提出一種基于偏最小二乘回歸的魯棒性特征選擇與分類算法(RFSC-PLSR)用于解決特征選擇中特征之間的冗余和多重共線性問題。首先,定義一個基于鄰域估計的樣本類一致性系數(shù);然后,根據(jù)不同k近鄰(k NN)操作篩選出局部類分布結(jié)構(gòu)穩(wěn)定的保守樣本,用其建立偏最小二乘回歸模型,進(jìn)行魯棒性特征選擇;最后,在全局結(jié)構(gòu)角度上,用類一致性系數(shù)和所有樣本的優(yōu)選特征子集建立偏最小二乘分類模型。從UCI數(shù)據(jù)庫中選擇了5個不同維度的數(shù)據(jù)集進(jìn)行數(shù)值實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明,與支持向量機(jī)(SVM)、樸素貝葉斯(NB)、BP神經(jīng)網(wǎng)絡(luò)(BPNN)和Logistic回歸(LR)四種典型的分類器相比,RFSC-PLSR在低維、中維、高維等不同情況下,分類準(zhǔn)確率、魯棒性和計算效率三種性能上均表現(xiàn)出較強(qiáng)的競爭力。
[Abstract]:A robust feature selection and classification algorithm based on partial least squares regression (PLS) is proposed to solve the redundancy and multiple collinearity problems between features in feature selection. Firstly, a class consistency coefficient based on neighborhood estimation is defined, then conservative samples with stable local class distribution structure are selected according to different k-nearest neighbor KNN operations, and the partial least square regression model is established. Finally, the partial least squares classification model is established by using the class consistency coefficient and the optimal feature subset of all samples in terms of global structure. Five data sets of different dimensions are selected from UCI database for numerical experiments. The experimental results show that compared with four typical classifiers, support vector machine (SVM), naive Bayesian BP neural network (BPNN) and Logistic regression (LRSR), RFSC-PLSR is of low dimension and middle dimension. The classification accuracy, robustness and computational efficiency are highly competitive under different conditions such as high dimension.
【作者單位】： 鄭州大學(xué)電氣工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(U1304602,61473266,61305080) 河南省高等學(xué)校重點(diǎn)科研項(xiàng)目(15A120016)~~
【分類號】：TP301.6

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 鐘龍權(quán);馬弘舸;;偏最小二乘回歸在微波效應(yīng)預(yù)測中的應(yīng)用[J];強(qiáng)激光與粒子束;2011年05期

2 汪春輝;羅飛;舒紅平;;偏最小二乘回歸在氣溫預(yù)測中的研究與應(yīng)用[J];微計算機(jī)信息;2012年05期

3 顏學(xué)峰;;基于徑基函數(shù)-加權(quán)偏最小二乘回歸的干點(diǎn)軟測量[J];自動化學(xué)報;2007年02期

4 朱洵;榮起國;;基于偏最小二乘回歸的基因網(wǎng)絡(luò)數(shù)學(xué)建模[J];系統(tǒng)仿真學(xué)報;2009年04期

5 董玉才;李紅燕;朱連軍;鞠桂玲;王素云;;基于偏最小二乘回歸的油田產(chǎn)量預(yù)測[J];信息系統(tǒng)工程;2009年10期

6 張，

本文編號：1775261